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Glioma, a common malignancy, is characterized by high morbidity and mortality. Promoting ferroptosis can delay tumor progression. Here, we aimed to explore the underlying mechanism of ferroptosis in glioma. In vitro and in vivo experiments were conducted using glioma cells and nude mice. The expression of genes and proteins was evaluated by RT‐qPCR, Western blot assay, and immunohistochemical staining. Malignant activities of glioma cells were evaluated using MTT, EdU, and Transwell assays. The levels of Fe2+, lipid reactive oxygen species, and malondialdehyde were determined using commercial kits. The interplays among CMTM5, WWP2, and LATS2 were validated using Co‐immunoprecipitation assay. The UALCAN database predicted downregulation of CMTM5 expression in glioma, and low expression of CMTM5 was associated with poor survival outcomes. CMTM5 overexpression inhibited cell growth and invasion and promoted ferroptosis of glioma cells. Besides, CMTM5 protein interacted with WWP2 protein and decreased WWP2 expression. WWP2 silencing attenuated LATS2 ubiquitination to enhance LATS2 expression and phosphorylation of YAP1. CMTM5 exerted a suppressive effect on cell growth and invasion and promoted ferroptosis of glioma cells by regulating the WWP2/LATS2 pathway. In the in vivo experiments, CMTM5 overexpression suppressed tumor growth and enhanced ferroptosis. CMTM5 regulated Hippo/YAP signaling to inhibit cell growth and invasion and to promote ferroptosis in glioma by regulating WWP2‐mediated LATS2 ubiquitination, thereby attenuating glioma progression. [ABSTRACT FROM AUTHOR]

Accurate estimation of nitrogen dioxide (NO2) levels at high spatio‐temporal resolution is crucial for atmospheric research and public health assessments. This study introduces a novel machine learning (ML) framework enhanced with uncertainty quantification (UQ) to predict hourly NO2 concentrations over East Asia. Benefiting from tropospheric NO2 columns from the Geostationary Environment Monitoring Spectrometer (GEMS), along with land use, meteorological, and topographic data, our model demonstrates robust and reliable performance, evidenced by an R2 of 0.84 and RMSE of 2.93 ppb through cross‐validation. Unlike traditional ML regressions tasks, our framework not only provides a single NO2 estimate but also diagnoses data and model uncertainties for each pixel. Higher uncertainties are observed in regions with complex terrain and sparse monitoring. Hourly and Intra‐annual variations in concentration predictions and associated uncertainties are both significant. The explicit UQ informs decision‐making and highlights the need for cautious interpretation in atmospheric data‐driven predictions. Plain Language Summary: Our study aims to improve understanding of near‐surface nitrogen dioxide (NO2) levels, crucial for accessing health risks. Machine learning (ML) has emerged as a powerful tool in converting satellite‐derived NO2 columns to near‐surface concentrations. However, previous estimates were based on polar‐orbiting satellites, which yield only a single daily result. In contrast, we use data products from the Geostationary Environment Monitoring Spectrometer (GEMS), the first sensor providing continuous monitoring over Asia with hourly updates on NO2 columns during daylight. Importantly, while these ML models proficiently predict NO2 levels at the stations, they tend to lack a comprehensive uncertainty assessment, leading to potential overreliance on their spatial performance. Our work developed a way to not only capture hourly variations of near‐surface NO2 concentrations but also to quantify the certainty about these estimates across the entire space. This enhances air quality decision‐making and underscores the need for careful environmental prediction analysis. Key Points: Quantify data and model uncertainties in machine learning, revealing data surpasses model—critical for accurate assessmentsDerive hourly NO2 concentrations from GEMS NO2 columns, refining spatial‐temporal resolutionInform site construction decisions, cautioning against overestimating machine learning's spatial generalization capabilities [ABSTRACT FROM AUTHOR]

Background: Numerous digestive system adverse events (dsAEs) have been observed during the use of anti-obesity medications (AOMs), leading to concerns about the safety of these medications. However, most current studies are limited to the association of one class of drugs with specific digestive disorders, and there is no cascading analysis of AOMs in the digestive system. This study aims to use data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS) for a stratified analysis of the reported associations between AOMs and dsAEs. Methods: We analyzed adverse event reports submitted to FAERS between January 2015 and December 2023 related to obesity treatment. It is important to note that FAERS data cannot establish causality or incidence rates. Pharmacovigilance (PV) signals were detected by disproportionate analyses through proportionate reporting ratio (PRR), reporting odds ratios (ROR), and information components (IC) to detect dsAEs associated with AOMs. Reporting rates, severity, and response outcomes of digestive adverse events were compared across AOMs by multivariate logistic regression analysis. Results: Among 34,396 adverse events (AEs) related to obesity treatment, 8844 dsAEs were analyzed. Comparing with semaglutide and liraglutide, tirzepatide exhibited fewer reported dsAEs while semaglutide and liraglutide showed a high correlation with non-lethal pancreatitis reports. Bupropion-naltrexone (31.65%) reported the highest number of dsAEs, and a PV signal was detected in mouth and lips AEs (ROR = 2.97, 95% CI: 2.42–3.6). Orlistat (ROR = 3.30, 95% CI: 3.08–3.55) exhibited the highest association with gastrointestinal AEs compared to other AOMs. PV signal for hepatobiliary AEs (ROR = 6.13, 95% CI: 3.45–10.88) with phentermine-topiramate still needs further clarification. Conclusions: Tirzepatide may be considered for patients with a history of digestive system disease or an elevated risk of pancreatitis based on the pattern of reported dsAEs. Caution is needed for the orofacial AEs when using bupropion-naltrexone. Orlistat has a higher reporting rate of gastrointestinal AEs, but these events are typically less severe. Phentermine-topiramate's association with liver impairment requires further clinical investigation. This article provides insights into the reported associations between AOMs and dsAEs, which may aid clinicians in making more informed decisions about individualizing medication and managing potential adverse events. [ABSTRACT FROM AUTHOR]

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images. [ABSTRACT FROM AUTHOR]

Water conservation is crucial for sustainable ecosystem development in loess gully regions. The redlines of permanent basic farmland protection and ecological protection are key land use control zones in China, yet their impact on water conservation is understudied. The study focuses on Yan'an and constructs three scenarios: natural development (NDS), farmland protection (FPS), and ecological protection (EPS). Then, land use simulation for the three scenarios in 2025, 2030, and 2035 is carried out using the patch-generating land use simulation model. Finally, the integrated valuation of ecosystem services and trade-offs model is combined to calculate water conservation. The findings suggest the following: (1) From the temporal perspective, the water conservation of the three scenarios exhibits an incremental trend by period from 2025 to 2035, with an annual growth rate of approximately 0.65‰ for the NDS, 0.60‰ for the FPS, and 0.64‰ for the EPS. (2) From the scenario perspective, from 2025 to 2035, the annual water conservation under the FPS is 0.35‰ less than that under the NDS, while the impact of the EPS is weak at only ±0.1‰. (3) Across all scenarios, the average water conservation depth of grassland surpasses that of forestland. This study provides scientific evidence to help optimize China's land space governance policy as well as methodological and theoretical support for related studies. [ABSTRACT FROM AUTHOR]

To address the heterogeneous clutter environment of airborne early warning radar in space-time adaptive processing (STAP), a direct data domain (D3) STAP method via joint-sparse characteristics of clutter and noise is proposed. Firstly, training sample set is expanded using the sub-aperture smoothing technique. Then, the STAP power dictionary matrix is constructed based on the second-order characterization theory of the clutter spectrum, and the objective function in derived. Meanwhile, the space-time power spectrum of the signal in the cell under test is solved. Finally, the clutter plus noise covariance matrix without aperture loss is reconstructed according to the clutter prior information. Numerical experiments show that the estimation accuracy of the proposed method is higher than that of the traditional sparse recovery D3-STAP algorithm, and the proposed method has better performance of the low-speed target detection under ideal conditions and array errors. [ABSTRACT FROM AUTHOR]