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Abstract Type 2 diabetes mellitus (T2DM) has been confirmed as an independent risk factor for colorectal cancer (CRC) in many studies. However, the mechanisms behind T2DM’s role in the progression of CRC remain unclear. This study aims to explore the potential biomarkers and molecular mechanisms involved in T2DM-promoted CRC progression. The limma package was used to identify differentially expressed genes in tumor tissue from CRC patients with or without T2DM. The key biological processes were screened by gene ontology and gene set enrichment analysis. A diagnostic model for co-morbidities was constructed by logistic regression model with least absolute shrinkage and selection operator (Lasso) regularization method. The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. The relationship of key genes with immune cells and other cells was evaluated by immune infiltration algorithm and single-cell transcription analysis. Drug prediction was performed by cMAP and the obtained drugs were molecularly docked with the key genes. The differentially expressed genes of T2DM-promoted CRC progression were mainly enriched to O-linked glycosylation-related processes. The diagnostic model constructed based on Lasso logistic regression had good diagnostic performance (AUC > 0.8). COX11 was the key gene for co-morbidities: in tumor tissues, COX11 expression was significantly higher than that in normal colon tissues. However, COX11 gene expression was significantly lower in patients with comorbidities than in patients without T2DM in tumor tissue. External datasets confirmed from both mRNA and protein expression levels that low COX11 expression was significantly associated with poor CRC prognosis. Immune infiltration analysis suggested that its expression related to the proportion of M2 macrophages. Single-cell transcriptome analysis revealed a close association of COX11 expression with endothelial cells and macrophages. The top4 drugs predicted bound well to COX11. Our study revealed that the pathogenesis of T2DM-promoted CRC progression related to O-linked glycosylation. We constructed a diagnostic model for T2DM-CRC co-morbidity. Meanwhile, we identified COX11 as a potential immune-related molecular marker closely associated with T2DM-promoted CRC progression. These mechanisms and molecular markers may provide new ideas for further studies of T2DM-promoted CRC progression and contribute to drug discovery for the treatment of co-morbidities.

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

Abstract High-amplitude quasi-stationary atmospheric Rossby waves with zonal wave numbers 6–8 associated with the phenomenon of quasi-resonant amplification (QRA) have been linked to persistent summer extreme weather events in the Northern Hemisphere. QRA is not well-resolved in current generation climate models, therefore, necessitating an alternative approach to assessing their behavior. Using a previously-developed fingerprint-based semi-empirical approach, we project future occurrence of QRA events based on a QRA index derived from the zonally averaged surface temperature field, comparing results from CMIP 5 and 6 (Coupled Model Intercomparison Project). There is a general agreement among models, with most simulations projecting substantial increase in QRA index. Larger increases are found among CMIP6-SSP5-8.5 (42 models, 46 realizations), with 85% of models displaying a positive trend, as compared with 60% of CMIP5-RCP8.5 (33 models, 75 realizations), with a reduced spread among CMIP6-SSP5-8.5 models. CMIP6-SSP3-7.0 (23 models, 26 realizations) simulations display qualitatively similar behavior to CMIP6-SSP5-8.5, indicating a substantial increase in QRA events under business-as-usual emissions scenarios, and the results hold regardless of the increase in climate sensitivity in CMIP6. Projected aerosol reductions in CMIP6-SSP3-7.0-lowNTCF (5 models, 16 realizations) lead to halting effect in QRA index and Arctic Amplification during the 1st half of the twenty-first century. Our analysis suggests that anthropogenic warming will likely lead to an even more substantial increase in QRA events (and associated summer weather extremes) than indicated by past analyses.

Abstract Colorectal cancer (CRC) is one of the most prevalent and lethal malignant tumors globally, posing significant health risks and societal burdens. Recently, advancements in next-generation sequencing technology have identified CRC intratumoral microbiota, thereby opening up novel avenues for further research. This review synthesizes the current advancements in CRC intratumoral microbiota and their impact on CRC progression and discusses the disparities in the relative abundance and community composition of CRC intratumoral microbiota across various colorectal tumors based on their anatomical location and molecular subtypes, as well as the tumor stages, and spatial tumor distribution. Intratumoral microbiota predominantly influence CRC development by modulating colonic epithelial cells, tumor cells, and the tumor microenvironment. Mechanistically, they can cause DNA damage, apoptosis and epithelial-mesenchymal transition. The effects of different intratumoral microbiota on CRC have been shown to be two-fold. In the future, to address the limitations of existing studies, it is important to develop comprehensive experimental protocols and suitable in vitro models for elucidating more mechanisms of intratumoral microbiota on CRC, which will facilitate the clinical application of microbe-related therapeutic strategies in CRC and potentially other tumors.

Abstract Colorectal cancer (CRC) is a common gastrointestinal malignancy with higher incidence and mortality rates in men compared to women, potentially due to the effects of estrogen signaling. There is substantial evidence supporting the significant role of 17β-Estradiol (E2) in reducing CRC risk in females, although this perspective remains debated. E2 has been demonstrated to inhibit CRC cell proliferation and migration at the cellular level by enhancing DNA mismatch repair, modulating key gene expression, triggering cell cycle arrest, and reducing activity of migration factors. Furthermore, E2 contributes to promote a tumor microenvironment unfavorable for CRC growth by stimulating ERβ expression, reducing inflammatory responses, reversing immunosuppression, and altering the gut microbiome composition. Conversely, under conditions of high oxidative stress, hypoxia, and nutritional deficiencies, E2 may facilitate CRC development through GPER-mediated non-genomic signaling. E2’s influence on CRC involves the genomic and non-genomic signals mediated by ERβ and GPER, respectively, leading to its dual roles in anticancer activity and carcinogenesis. This review aims to summarize the potential mechanisms by which E2 directly or indirectly impacts CRC development, providing insights into the phenomenon of sexual dimorphism in CRC and suggesting potential strategies for prevention and treatment. Graphical Abstract

ABSTRACTInverting grassland above-ground biomass (AGB) presents a significant challenge due to difficulties in characterizing leaf physiological states and obtaining accurate ground-truth data. This study introduces an innovative hybrid model for AGB inversion based on the AGB = leaf mass per area (LMA) * leaf area index (LAI) paradigmn in the Ewenki Banner region of Inner Mongolia. The model integrates the PROSAIL radiative transfer model, machine learning regression, LEnKF data assimilation theory, multisource remote sensing, and meteorological data, following a four-step approach. Firstly, we establish LAI and LMA inversion models by combining the PROSAIL model with machine learning techniques. Secondly, data assimilation fuses the PROSAIL-derived LAI with MODIS-LAI. In the third phase, a Random Forest predictive model is developed for LMA estimation. Lastly, the accuracy of the hybrid model is assessed using empirical data. Precision evaluation with ground-truth samples demonstrates that the assimilated LAI and RF-predicted LMA yield the lowest prediction error for grassland AGB (RMSE = 0.0033 g/cm2; MAE = 0.0028 g/cm2). This model framework addresses the challenge of limited prior knowledge in the PROSAIL-AGB prediction model, thereby enhancing the prediction accuracy while maintaining its key advantages: providing continuous observations at high spatiotemporal resolutions without relying on measured sample data.

Sex differences in colorectal cancer (CRC) has received considerable research attention recently, particularly regarding the influence of sex hormones and the intestinal microbiota. Estrogen, at the genetic and epigenetic levels, directly inhibits CRC cell proliferation by enhancing DNA mismatch repair, regulating miRNAs, blocking the cell cycle, and modulating ion channels. However, estradiol’s activation of GPER promotes oncogene expression. Conversely, androgen contributes to epigenetic dysregulation and CRC progression via nuclear receptors while inducing apoptosis through membrane receptors. Specific gut microorganisms produce genotoxins and oncogenic metabolites that damage colonic cell DNA and contribute to cancer induction. Regarding the tumor microenvironment, estrogen mitigates intestinal inflammation, reverses immunosuppression, increases gut microbiome diversity and commensal bacteria abundance, and decreases pathogen enrichment. On the contrary, androgen disrupts intestinal microecology, diminish immunotherapy efficacy, and exacerbate colonic inflammation and tumor growth. The impact of estrogen and androgen is closely tied to their receptor status, elucidating their dual roles in CRC pathogenesis. This review comprehensively discusses the direct and indirect effects of sex hormones and the intestinal microbiota on CRC, considering environmental factors such as diet and lifestyle to propose novel prevention and treatment strategies.

The incidence and mortality rates of colorectal cancer have elevated its status as a significant public health concern. Recent research has elucidated the crucial role of mitochondrial fusion–fission dynamics in the initiation and progression of colorectal cancer. Elevated mitochondrial fission or fusion activity can contribute to the metabolic reprogramming of tumor cells, thereby activating oncogenic pathways that drive cell proliferation, invasion, migration, and drug resistance. Nevertheless, excessive mitochondrial fission can induce apoptosis, whereas moderate mitochondrial fusion can protect cells from oxidative stress. This imbalance in mitochondrial dynamics can exert dual roles as both promoters and inhibitors of colorectal cancer progression. This review provides an in‐depth analysis of the fusion–fission dynamics and the underlying pathological mechanisms in colorectal cancer cells. Additionally, it offers partial insights into the mitochondrial kinetics in colorectal cancer‐associated cells, such as immune and endothelial cells. This review is aimed at identifying key molecular events involved in colorectal cancer progression and highlighting the potential of mitochondrial dynamic proteins as emerging targets for pharmacological intervention.

Abstract Background Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. Methods We conducted a ‘Microbiome with pulmonary nodule series study 1’ (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. Results The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. Conclusions Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. Trial registration Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Abstract Sexual dimorphism has been observed in the incidence and prognosis of colorectal cancer (CRC), with men generally exhibiting a slightly higher incidence than women. Research suggests that this difference may be attributed to variations in sex steroid hormone levels and the gut microbiome. The gut microbiome in CRC shows variations in composition and function between the sexes, leading to the concept of ‘microgenderome’ and ‘sex hormone–gut microbiome axis.’ Conventional research indicates that estrogens, by promoting a more favorable gut microbiota, may reduce the risk of CRC. Conversely, androgens may have a direct pro-tumorigenic effect by increasing the proportion of opportunistic pathogens. The gut microbiota may also influence sex hormone levels by expressing specific enzymes or directly affecting gonadal function. However, this area remains controversial. This review aims to explore the differences in sex hormone in CRC incidence, the phenomenon of sexual dimorphism within the gut microbiome, and the intricate interplay of the sex hormone–gut microbiome axis in CRC. The objective is to gain a better understanding of these interactions and their potential clinical implications, as well as to introduce innovative approaches to CRC treatment. Graphical Abstract

Abstract Colorectal cancer (CRC) is a significant public health concern, and its development is associated with mitochondrial dysfunction. Mitochondria can adapt to the high metabolic demands of cancer cells owing to their plasticity and dynamic nature. The fusion–fission dynamics of mitochondria play a crucial role in signal transduction and metabolic functions of CRC cells. Enhanced mitochondrial fission promotes the metabolic reprogramming of CRC cells, leading to cell proliferation, metastasis, and chemoresistance. Excessive fission can also trigger mitochondria-mediated apoptosis. In contrast, excessive mitochondrial fusion leads to adenosine triphosphate (ATP) overproduction and abnormal tumor proliferation, whereas moderate fusion protects intestinal epithelial cells from oxidative stress-induced mitochondrial damage, thus preventing colitis-associated cancer (CAC). Therefore, an imbalance in mitochondrial dynamics can either promote or inhibit CRC progression. This review provides an overview of the mechanism underlying mitochondrial fusion–fission dynamics and their impact on CRC biology. This revealed the dual role of mitochondrial fusion–fission dynamics in CRC development and identified potential drug targets. Additionally, this study partially explored mitochondrial dynamics in immune and vascular endothelial cells in the tumor microenvironment, suggesting promising prospects for targeting key fusion/fission effector proteins against CRC. Graphical Abstract

Accurate delineation of paddy fields holds importance in ensuring food security, efficient water resource management, and precise evaluation of greenhouse gas emissions. Here we propose an innovative approach, the asynchronous flooding and object-based (AF-OB) model, aimed at optimizing phenology-based paddy field mapping. The AF-OB model capitalizes on the asynchronous flooding phenomenon observed between paddy fields and nonpaddy fields, along with the seasonal variations in the normalized difference vegetation index. The simple noniterative clustering algorithm is integrated to mitigate the common issue of the “pretzel effect” encountered in paddy field mapping. Evaluation through independent samples yields compelling results, with the paddy field map generated by the AF-OB method achieving an overall accuracy of 94.28%. The paddy fields extracted using the AF-OB method exhibit alignment with statistical data, surpassing comparable algorithms relying on alternative land use products in terms of visual quality. Furthermore, the AF-OB model exhibits stability across time, space, and sensors, thus enhancing its applicability and robustness. The outputs of the AF-OB method offer reference data for informed agricultural production planning and the effective management of water resources.

Monitoring the dynamic distribution of irrigated cropland and assessing its cooling effects are essential for advancing sustainable agriculture amid climate change. This study presents an integrated framework for irrigated cropland monitoring and cooling effect assessment. Leveraging dense time series vegetation indices with Google Earth Engine (GEE), we evaluated multiple machine learning algorithms within to identify the most robust approach (random forest algorithm) for mapping irrigated cropland in Inner Mongolia from 2010 to 2020. Furthermore, we developed an effective method to quantify the diurnal, seasonal, and interannual cooling effects of irrigation. Our generated irrigated cropland maps demonstrate high accuracy, with overall accuracy ranging from 0.85 to 0.89. This framework effectively captures regional cropland expansion patterns, revealing a substantial increase in irrigated cropland across Inner Mongolia by 27,466.09 km2 (about +64%) between 2010 and 2020, with particularly pronounced growth occurring after 2014. Analysis reveals that irrigated cropland lowered average daily land surface temperature (LST) by 0.25 °C compared to rain-fed cropland, with the strongest cooling effect observed between July and August by approximately 0.64 °C, closely associated with increased evapotranspiration. Our work highlights the potential of satellite-based irrigation monitoring and climate impact analysis, offering a valuable tool for supporting climate-resilient agriculture practices.

Abstract The response of terrestrial water storages to dryland vegetation growth remains poorly understood. Using multiple proxies from satellite observations and model outputs, we show an overall increase (decrease) in vegetation growth (terrestrial water storages) across drylands globally during 1982–2016. Terrestrial water storages in greening drylands correlate negatively with vegetation growth, particularly for cropland-dominated regions, and such response is pronounced when the growth rate of vegetation productivity is high. Reduction in terrestrial water storage is dominated by precipitation and evapotranspiration variability rather by than runoff. We predict reduction in terrestrial water storage of 41–84% by 2100, accompanying expansion of drylands by 4.1–10.6%. Our findings, which indicate sustained adverse response of terrestrial water storage to vegetation growth in drylands, highlight the need for concerted planning for balanced ecological restoration, agricultural management, and water resource utilization that will affect 5.17 billion people, 64% of whom live in developing countries.

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies.

Abstract Accurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, restricting their applicability on a national scale and under future climate change scenarios. We present a robust framework leveraging machine learning and multiple data sets to estimate IWU across China. Forced with an ensemble of climate and socio‐economic projections, we appraise future trends and additional costs of IWU. Our model shows high accuracy in reproducing IWU, with coefficient of determination (R2) ranging from 0.86 to 0.91 and root mean square error from 0.261 to 0.361 km3/yr when compared to reported values in Chinese prefectures. Independent validation at 11 cropland sites further confirms the model's predictive power (R2 = 0.67). Under different emissions scenarios, China's IWU is projected to increase by 8.5%–17.1% (6.8%–34.8%) by 2050s (2100s) compared to the historical period (1981–2010), with higher emissions leading to more significant increases. This rise in IWU by 2050s (2100s) comes with an estimated additional cost of US $1.65–3.91 ($2.28–6.5) billion/year, highlighting the urgency for sustainable water management. Our study provides an effective approach for estimating current and future IWU using machine learning techniques, transferable to other countries facing increasing irrigation demands.

The prevalence of colorectal cancer is increasing worldwide, and despite advances in treatment, colorectal cancer (CRC) remains in the top three for mortality due to several issues, including drug resistance and low efficiency. There is increasing evidence that baicalin and baicalein, novel small molecule inhibitor extracts of the Chinese herb Scutellaria baicalensis, have better anti-colorectal cancer effects and are less likely to induce drug resistance in cancer cells. The present review article explains the anti-proliferative properties of baicalin and baicalein in the context of against CRC. Additionally, it explores the underlying mechanisms by which these compounds modulate diverse signaling pathways associated with apoptosis, cell proliferation, tumor angiogenesis, invasion, metastasis, and tumor microenvironment. Moreover, this review article highlights the inhibitory effect of colorectal inflammatory-cancer transformation and the near-term therapeutic strategy of using them as adjuvant agents in chemotherapy.

BackgroundObservational studies have indicated associations between type 2 diabetes mellitus (T2DM) and both colorectal cancer (CRC) and inflammatory bowel disease (IBD). However, the underlying causality and biological mechanisms between these associations remains unclear.MethodsWe conducted a bidirectional Mendelian randomization (MR) analysis employing summary statistics from genome-wide association studies involving European individuals. The inverse variance weighting (IVW) method was the primary method used to assess causality. Additionally, we applied MR Egger, Weighted median, Simple mode, and Weighted mode to evaluate the robustness of the results. Outliers were identified and eliminated using the MR-PRESSO, while the MR-Egger intercept was used to assess the horizontal pleiotropic effects of single nucleotide polymorphisms (SNPs). The heterogeneity was evaluated using the Cochrane Q test, and sensitivity analysis was performed using leave-one-out method. The F statistic was calculated to evaluate weak instrumental variable bias. Finally, a pilot bioinformatics analysis was conducted to explore the underlying biological mechanisms between T2DM and IBD/UC.ResultsThe IVW results demonstrated that T2DM significantly reduced risks of IBD (OR=0.885, 95% CI: 0.818–0.958, P=0.002) and ulcerative colitis (UC) (OR=0.887, 95% CI: 0.812–0.968, P=0.007). Although the 95% CIs of MR Egger, Weighted median, Simple mode, and Weighted mode were broad, the majority of their estimates were consistent with the direction of IVW. Despite significant heterogeneity among SNPs, no horizontal pleiotropy was observed. The leave-one-out analysis showed that the causality remained consistent after each SNP was removed, underscoring the reliability of the results. Reverse MR analysis indicated that genetic susceptibility to both CRC and IBD had no significant effect on the relative risk of T2DM. Ten hub genes were identified, which mainly enriched in pathways including maturity onset diabetes of the young, thyroid cancer, gastric acid secretion, longevity regulating pathway, melanogenesis, and pancreatic secretion.ConclusionThe presence of T2DM does not increase the risk of CRC or IBD. Moreover, T2DM might reduce risk of IBD, including UC. Conversely, the occurrence of CRC or IBD does not influence the risk of T2DM. The association between T2DM and IBD/UC may be related to the changes in multiple metabolic pathways and CTLA-4-mediated immune response.

Background: The global increase in an aging population has led to more common age-related health challenges, particularly multimorbidity and frailty, but there is a significant gap. Methods: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (1999–2018). The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted multivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested cross-validation and tested with various algorithms including decision tree, Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AU-PRC) to evaluate six algorithms, select the optimal model, and test the discrimination and consistency of the optimal model. Results: The study included 46,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cross-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration. Conclusions: We found 49 years maintain the balance of physiological reserve and external aggression. In addition, chronic diseases are trigger factor of frailty, while acute diseases are contributing factor that exacerbates the body's rapid decline. Last, the XGBoost frailty prediction model, with its simplicity, high performance and high clinical value holds potential for clinical application.

Abstract Background Bone marrow-derived mesenchymal stem cells (BMSCs)-derived extracellular vesicles (EVs) have shown potent anti-inflammatory function in various pathological conditions, such as osteoarthritis and neurodegenerative diseases. Since the number of EVs naturally secreted by cells is finite and they usually bear specific repertoires of bioactive molecules to perform manifold cell–cell communication, but not one particular therapeutic function as expected, their practical application is still limited. Strategies are needed to increase the production of EVs and enhance their therapeutic function. Recent studies have suggested that low-intensity pulsed ultrasound (LIPUS) is a promising non-invasive method to increase the secretion of EVs and promote their anti-inflammatory effects. However, the effect of LIPUS stimulation of BMSCs on EVs derived from the cells remains unclear. The objective of this study was to investigate whether LIPUS stimulation on BMSCs could increase the secretion of EVs and enhance their anti-inflammatory effects. Methods BMSCs were exposed to LIPUS (300 mW/cm2) for 15 min and EVs were isolated by ultracentrifugation. Anti-inflammatory effects of EVs were investigated on RAW264.7 cells in vitro and in the allogeneic skin transplantation model. Small RNA-seq was utilized to identify components difference in EVs with/without LIPUS irradiation. Results In this study, we found that LIPUS stimulation could lead to a 3.66-fold increase in the EVs release from BMSCs. Moreover, both in vitro and in vivo experimental results suggested that EVs secreted from LIPUS-treated BMSCs (LIPUS-EVs) possessed stronger anti-inflammatory function than EVs secreted from BMSCs without LIPUS stimulation (C-EVs). RNA-seq analysis revealed that miR-328-5p and miR-487b-3p were significantly up-regulated in LIPUS-EVs compare with C-EVs. The suppression of MAPK signaling pathway by these two up-regulated miRNAs could be the potential mechanism of strengthened anti-inflammatory effects of LIPUS-EVs. Conclusion LIPUS stimulation on BMSCs could significantly increase the secretion of EVs. Moreover, EVs generated from LIPUS-treated BMSCs possessed much stronger anti-inflammatory function than C-EVs. Therefore, LIPUS could be a promising non-invasive strategy to promote the production of EVs from BMSCs and augment their anti-inflammatory effects.

Hyaluronic acid (HA), as a hygroscopic and biocompatible molecule, has displayed unique permeation enhancement in transdermal delivery systems. Hence, indomethacin (IND) was encapsulated in HA-modified transfersomes (IND-HTs) to enhance transdermal IND delivery to reduce adverse effects in this study. The physiochemical properties of IND-HTs were characterized. Results showed that the prepared IND-HTs were spherical and revealed good entrapment efficiency (87.88 ± 2.03%), with a nanometric particle size (221.8 ± 93.34 nm). Then, IND-HTs were further incorporated into a carbopol 940 hydrogel (IND-HTs/Gel) to prolong retention capacity on the skin. The in vitro release and skin permeation experiments of IND-HTs/Gel were carried out with the Franz diffusion cells. It was found that IND-HTs/Gel exhibited sustained drug release, as well as superior drug permeation and flux across the skin. Confocal laser scanning microscopy showed improved penetration of HTs/Gel with a wider distribution and higher fluorescence intensity. The hematoxylin–eosin stained showed that HA improved the transdermal effect by changing the microstructure of skin layers and decreasing skin barrier function. In addition, IND-HTs/Gel showed significant analgesic activity in hot plate test and no potentially hazardous skin irritation. This study indicated that the developed IND-HTs/Gel could be a promising alternative to conventional oral delivery of IND by topical administration.

Human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) is the leading cause of cancer death in women. For patients with HER2-positive MBC, after the failure of multiple lines of treatment, there is no optimal line of therapy. A series of clinical trials confirmed that treatment with irreversible pan-HER tyrosine kinase inhibitors (TKIs) in combination with chemotherapy significantly improves patients’ survival outcomes. This review focuses on the pathogenesis of HER2-positive breast cancer, current standard treatments, mechanisms of approved irreversible TKIs, and key clinical trials. The available findings suggest that irreversible pan-HER TKIs, such as pyrotinib and neratinib, in combination with chemotherapy, represent a beneficial salvage therapy for patients with HER2-positive MBC with manageable toxicity. However, further studies are needed to assess the efficacy and safety of this combination therapy.

Abstract Background This study aimed to determine the efficacy and complications of intravitreal chemotherapy-assisted endoresection for refractory International Classification of Retinoblastoma (ICRB) group D retinoblastoma in monocular patients. Methods In this retrospective case series, intravitreal chemotherapy-assisted endoresection by pars plana vitrectomy was performed in 11 eyes with refractory ICRB group D retinoblastoma unresponsive to standard therapies in monocular patients. Results Across a mean follow-up period of 42.7 months, globe salvage was attained in all 11 eyes (100%). There were no cases of extra-ocular tumour seeding or remote metastasis. In 9 eyes (81.8%), tumour control was achieved with one pars plana vitrectomy; in 2 cases (18.2%), repeated treatment, such as laser therapy, intravitreal chemotherapy or a second pars plana vitrectomy, was needed. Retinal reattachment was achieved in all 4 eyes (100%) with previous retinal detachment. Four eyes (36.4%) required subsequent cataract surgery due to secondary cataract. Ten eyes (90.9%) had improvement in best-corrected visual acuity at the last follow-up. Conclusion Intravitreal chemotherapy-assisted endoresection appears to be a safe and effective globe-salvaging method for refractory group D retinoblastoma. It is a promising alternative to enucleation and a supplementary therapeutic strategy for those unresponsive to standard therapies, especially for the monocular retinoblastoma patients.

The response of the urban thermal environment to green space landscapes has been studied previously, while its detailed pattern is insufficiently explored owing to the constraints of spatial and temporal resolution of available datasets. This study uses integrated Thermal Airborne Spectrographic Imager (TASI) data, Landsat TM/ETM and MODIS satellite imagery, and Noah land surface model output to investigate the effects of landscape pattern on the urban thermal environment across Shijiazhuang, China. The present study not merely proposes a generalized framework for the spatiotemporal analysis of urban thermal environment, but also affords several insights into the cooling effects related to urban green space landscapes in semiarid cities. Firstly, trees and lawns show a noticeable disparity in land surface temperature (LST) response to urban green landscape metrics, primarily due to the difference in cooling efficiency via evapotranspiration. This disparity can be explained further by the radiation-shading effect of trees. Secondly, analysis confirms that the composition of urban green space has a substantial impact on LST throughout summer. This pattern is largely stable for trees owing to the constant Bowen ratio, but they are altered for lawns. Conversely, the configurations of urban green space exhibit less impact on LST. These effects vary temporally in magnitude and can be enhanced notably in humid conditions. The lower correlation between the configuration metrics of urban green and LST has to do with the surface resistance alterations and additional cooling effects. Finally, consistent patterns of the impact of urban green space landscape metrics on LST are illustrated at different spatial scales and region sizes but greater effects are revealed for smaller analytical units, further confirming the impacts of urban green landscape on urban thermal environment.

Drought, water shortage, and anthropogenic disturbance bring about serious ecological issues in the Yellow River Basin. In recent decades, the “Grain-for-Green” project, wind-sand control, and water ecological civilization construction are major events affecting the ecological vulnerability in the Yellow River Basin. Based on the “exposure-sensitivity-adaptability” framework and earth observation data, this study proposes an ecological vulnerability evaluation index system that reflects the impacts of natural and human stressors and assesses the ecological vulnerability in the Yellow River Basin during different policy periods. The results show that the overall ecological vulnerability level shows a decreasing trend from 2001 to 2019 in the Yellow River Basin, despite a slight increase in 2015 due to the impact of the dry climate. Medium ecological vulnerability level prevails in the Yellow River Basin. The vulnerability levels have obvious geographical spatial variation. The northern areas in the upper reaches are most vulnerable. Ecological vulnerability in the midstream is better than that in downstream. Ecological restoration policies had an overall positive influence on ecological vulnerability improvement. From 2001 to 2010, the percentages of Heavy (0.60–0.70) and Very heavy (≥0.70) ecological vulnerability areas in upstream and midstream decreased by 13.3 % under the impact of priority forestry projects. The findings provide guidance for ecological restoration in the Yellow River Basin and the outlined approach is potentially transferable to assess the ecological vulnerability in other regions.

Red mud desulfurization is an environmentally friendly desulfurization technology. After desulfurization, the acidity of red mud slurry continues to be neutralized for processing new red mud, and no waste acid is generated. At present, there is a lack of research on desulfurization intensification in external fields, etc. To further enhance red mud desulfurization, this paper used an SO2 detector, X-ray fluorescence spectrometer (XRF), and scanning electron microscope (SEM) to compare and analyze red mud desulfurization under the action of ball mill and ultrasonic external fields. In this study, experiments were conducted using a bubbling and stirring reactor device. The results showed that the suitable red mud slurry concentration was 10 g/L. The raw red mud desulfurization (without external field condition) could reach 100% absorption in the first 25 min, and the desulfurization rate dropped to 81.3% at 80 min. The mechanism of red mud desulfurization was investigated by X-ray diffractometer (XRD), XRF, and infrared spectroscopy. Under the action of the external field of the ball mill, the red mud particles could be refined to prolong the desulfurization time. The red mud after ball milling could reach 100% absorption within 33 min. Under the thermal effect of the ultrasound, 100% absorption could only be achieved within 23 min. From the desulfurization effect and XRF results, it was found that the ball mill was more suitable for promoting red mud desulfurization in the bubbling and stirring reactor.

Urbanization affects the regional hydrological cycle, but quantitative assessment of the hydrological regime in regions of high urbanization remains unexplored. This study investigates the response of runoff to impervious surface area (ISA) dynamics across three highly urbanized cities in southern China. The Soil Conservation Service Curve Number hydrologic model is modified by coupling the 1-km high-resolution calibrated precipitation data and 250-m-resolution percentages of remotely sensed land cover. Results demonstrate that runoff metrics increase noticeably during 2000–2017 at the city scale, with larger (smaller) magnitudes observed for high-flow (low-flow) runoff metrics. Runoff trend variations are more pronounced during high-flow seasons and are remarkable over land surfaces. Analyses of linear regression and generalized additive models indicate that ISA changes account for 23–27% of the total variance of increased runoff. The ascending trend of runoff is not affected substantially by the slight decrease in ISA during 2014–2017, which is attributed to the combined influence of land cover changes and climate. At the subwatershed scale, changes in ISA explain 17% of runoff variations. The runoff regime is more (less) responsive at the high (low) ISA percentage, and one threshold indicative of substantial modification to runoff is detected in the ISA percentage. Overall, runoff responses identified in the spatiotemporal analysis underscore the importance of monitoring and assessing the hydrological regime in southern China as urbanization progresses.

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.

Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution of the soil moisture dataset often hinders its use in local or regional research. This work proposes a new framework based on the random forest (RF) model while using five auxiliary data to downscale the AMSR-E soil moisture data over North China. The downscaled results with a 1 km spatial resolution are verified against in situ measurements. Compared with AMSR-E data, the correlation coefficient of the downscaled data is increased by 0.17, and the root mean squared error, mean absolute error, and unbiased root mean square error are reduced by 0.02, 0.01, and 0.03 m3/m3, respectively. In addition, the comparison results with Multiple Linear Regression and Support Vector Regression downscaled data show that the proposed method significantly outperforms the other two methods. The feasibility of our model is well supported by the importance analysis and leave-one-out analysis. Our study, which combines RF with spatiotemporal search algorithms and efficient auxiliary data, may provide insights into soil moisture downscaling in large areas with various surface characteristics and climatic conditions.

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation.

Groundwater resources consumption and management play a crucial role in food-energy- water nexus. However, the trends in groundwater storage variability and its attribution remain unclear because of the combined effects of climatic and anthropogenic terms. Here we use satellites and monitoring well observations to reveal the trends in groundwater storage change (GWSC), which exhibits geographical heterogeneity over the southeast side of the Hu Line in China during 1979–2012. The GWSC in northern China showed a slight decrease from 1979 to 1997, and the declining pattern extended to surrounding regions from 1998 to 2012. A considerable fraction of the GWSC trend can be attributed jointly to precipitation variations and human water usage. The anthropogenic factors that are primarily associated with socioeconomic development contribute to ∼31% of the variability in GWSC. Water management policies carried out in recent years reasonably explain the recovery of GWSC across regions with declining groundwater in 2013–2019. A positive trend in GWSC is further projected (2020–2029), though with uncertainties.

Understanding the driving forces of land use/cover change (LUCC) is a requisite to mitigate and manage effects and consequences of LUCC. This study aims to analyze drivers of LUCC in New England, USA. It combines meta-study, GIS, and machine learning to identify the important factors of LUCC in the area. Firstly, we conducted a meta-study of the research on LUCC in the New England area and specifically focused on the driving forces analysis. The meta-analysis revealed that the LUCC studies in the research area were highly related with many other research topics, and population and economic factors were the most mentioned drivers of the LUCC. The drivers of LUCC in this study area for the past several decades were relatively well analyzed. However, the study of the main driving forces of recent LUCC is lacking. Then, the determinants of LUCC for the recent years were quantitatively assessed using the random forests (RF) model along with geospatial data processing. Two planning regions in Connecticut and one planning region in Massachusetts were selected to serve as the case study areas. Investigated variables included environmental and biophysical variables, location measures of infrastructure and existing land use, political variables, and demographic and social variables. These drivers were examined for their relations with LUCC processes. Their importance as driving forces was ranked by the RF method. The results show both consistency and inconsistency between the meta-analysis and the RF method. We found that this mixed method can enhance our understanding of driving forces of LUCC and improve the selection quality of important drivers for modeling LUCC. With more solid information, better land management advices for sustainable development may also be provided.

The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.

Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember Spectral Mixture Analysis (MESMA)-derived methods) that are commonly used to map VFC based on Landsat imagery, in modeling surface heat fluxes in urban landscape. For this purpose, two different heat flux models, Two-source energy balance (TSEB) model and Pixel Component Arranging and Comparing Algorithm (PCACA) model, were adopted for model evaluation and analysis. A comparative analysis of the NDVI-derived and MESMA-derived VFCs showed that the latter achieved more accurate estimates in complex urban regions. When the two sources of VFCs were used as inputs to both TSEB and PCACA models, MESMA-derived urban VFC produced more accurate urban heat fluxes (Bowen ratio and latent heat flux) relative to NDVI-derived urban VFC. Moreover, our study demonstrated that Landsat imagery-retrieved VFC exhibited greater uncertainty in obtaining urban heat fluxes for the TSEB model than for the PCACA model.

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.

We find strong path dependence in the evolution of the Plio--Pleistocene glaciations using CLIMBER-2 Earth System Model simulations from the mid-Pliocene to modern preindustrial (3 My-0 My BP) driven by a gradual decrease in volcanic carbon dioxide outgassing and regolith removal from basal ice interaction. Path dependence and hysteresis are investigated by alternatively driving the model forward and backward in time. Initiating the model with preindustrial conditions and driving the model backward using time-reversed forcings, the increase in volcanic outgassing back-in-time (BIT) does not generate the high CO2 levels and relatively ice-free conditions of the late Pliocene seen in forward-in-time (FIT) simulations of the same model. This behavior appears to originate from nonlinearities and initial state dependence in the carbon cycle. A transition from low-amplitude sinusoidal obliquity (~41 ky) and precession (~23 ky) driven glacial/ interglacial cycles to high-amplitude ~100 ky likely eccentricity-related sawtooth cycles seen between -1.25 My and -0.75 My BP (the Mid-Pleistocene transition or "MPT") in FIT simulations disappears in BIT integrations depending on the details of how the regolith removal process is treated. A transition toward depleted regolith and lowered atmospheric CO2 levels are both required to reproduce the MPT. [ABSTRACT FROM AUTHOR]

In gene therapy, intravenous injection of viral vectors reigns as the primary administration route. These vectors include adeno-associated viruses, adenoviruses, herpes viruses, rhabdoviruses and others. However, these naturally occurring viruses lack inherent tissue or organ tropism for tailored disease treatment. To address this, we devised an optimized process involving directed viral capsid evolution, organ-specific humanized mouse models and in vitro-in vivo virus screening. Our approach allows for the rapid generation specifically modified adeno-associated virus variants, surpassing the time required for natural evolution, which spans millions of years. Notably, these variants exhibit robust targeting of the liver, favouring chimeric human liver cells over murine hepatocytes. Furthermore, certain variants achieve augmented targeting with reduced off-target organ infection, thereby mitigating dosage requirements and enhancing safety in gene therapy. [ABSTRACT FROM AUTHOR]

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest "Heat Dome" event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event. [ABSTRACT FROM AUTHOR]