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Complicated nano- and atomic-scale processes with sub-angstrom spatial resolution and millisecond time resolution visualized by in situ transmission electron microscopy (TEM) are often highly dynamical and time consuming to analyze and interpret. Here, we report how variational autoencoders (VAEs) can provide an artificial intelligence's interpretation of high-resolution in situ TEM data by condensing and deconvoluting complicated atomic-scale dynamics into a latent space with reduced dimensionality. We designed a VAE model with high latent dimensions capable of deconvoluting information from complex high-resolution TEM data. We demonstrate how this model, with high latent dimensions trained on atomically resolved TEM images of lead sulfide (PbS) nanocrystals, is able to capture movements and perturbations of periodic lattices in both simulated and real in situ TEM data. The VAE model shows the capability of detecting and deconvoluting dynamical nanoscale physical processes, such as the rotation of crystal lattices and intra-particle ripening during the annealing of semiconductor nanocrystals.

BackgroundCompared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.ObjectivesThis study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.MethodsSES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.ResultsThe accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.ConclusionStreet view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Abstract Background Though bictegravir/emtricitabine/tenofovir (BIC/FTC/TAF) have been regulatory approved and included in the National Reimbursement Drug List in China, due to the affordability concern, generic version of efavirenz + lamivudine + tenofovir (EFV + 3TC + TDF) is still recommended as the first-line therapy in the clinical guideline and widely used in clinical practice. The aim of the study is to assess the persistence with first-line BIC/TAF/TAF and EFV + 3TC + TDF in newly treated HIV-1 patients in the real-world setting in Hunan Province in China. Methods A retrospective analysis of the medical records of HIV patients initiating first-line antiretroviral therapy in the First Hospital of Changsha in January 1st, 2021-July 31st, 2022 was conducted. Persistence was assessed as the number of days on the therapy from the index until treatment discontinuation or end of data availability. Kaplan-Meier Curves and Cox Proportional Hazard models were used to evaluate the discontinuation rates. Subgroup analysis was performed excluding BIC/FTC/TAF patients with treatment discontinuation due to economic reason, and EFV + 3TC + TDF patients with a viral load > 500,000 copies/mL. Results A total of 310 eligible patients were included in the study, with 244 and 66 patients in the BIC/FTC/TAF group and EFV + 3TC + TDF group, respectively. Compared with EFV + 3TC + TDF patients, BIC/FTC/TAF patients were older, more living in the capital city currently, and had significantly higher total cholesterol and low-density level (all p 500,000 copies/mL, the analysis showed similar results (HR = 10.1, 95% CI = 1.2–84.1). 79.4% of the EFV + 3TC + TDF patients discontinued treatment due to clinical reasons, while 83.3% of the BIC/FTC/TAF patients discontinued treatment due to economic reasons. Conclusions Compared with BIC/FTC/TAF, EFV + TDF + 3TC patients were significantly more likely to discontinue the first-line treatment in Hunan Province in China.

Colloidal superlattices are fascinating materials made of ordered nanocrystals, yet they are rarely called "atomically precise". That is unsurprising, given how challenging it is to quantify the degree of structural order in these materials. However, once that order crosses a certain threshold, the constructive interference of X-rays diffracted by the nanocrystals dominates the diffraction pattern, offering a wealth of structural information. By treating nanocrystals as scattering sources forming a self-probing interferometer, we developed a multilayer diffraction method that enabled the accurate determination of the nanocrystal size, interparticle spacing, and their fluctuations for samples of self-assembled CsPbBr3 and PbS nanomaterials. The multilayer diffraction method requires only a laboratory-grade diffractometer and an open-source fitting algorithm for data analysis. The average nanocrystal displacement of 0.33 to 1.43 Å in the studied superlattices provides a figure of merit for their structural perfection and approaches the atomic displacement parameters found in traditional crystals.

Increasing access to undergraduate research is critical in efforts to retain students pursuing careers in STEM. Alternatives to traditional research positions, such as course based undergraduate research experiences (CUREs), have played important roles in engaging more undergraduates in research. However, these opportunities are only a subset of possible nontraditional research experiences which can enrich the undergraduate experience. In this study, we critically examined and improved the research-group-led undergraduate research program (GURP) to better meet the needs of undergraduates seeking research. Specifically, we investigated if the program was successful with a greater diversity of data sets, if assessments of learning were consistent across different data sets and teaching environments, and if the program promoted student engagement in research. We have found that this model is scalable, robust, and adaptable to different implementations while producing consistent and positive learning outcomes for students. Especially remarkable are students' increased self-identification as scientists and statistically significant gains in self-perceived competency across multiple domains of knowledge. This program model has shown promising results as a partially and fully online research experience for undergraduates and has benefited program alumni in their research careers. To assist research groups starting similar programs, we have created public data sets and instructional resources. We believe that GURP programs can work in a variety of situations and hope that they can become a tool to increase interest and build communities for young researchers.

The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.

The fluid distribution is considerably influenced by the reservoir homogeneity. This study analyzes carbonate reservoir heterogeneity and hydrocarbon accumulation processes in the Khasib reservoirs in AB oilfield in the Middle East to discuss factors controlling the distribution of fluid and the effects of this distribution on the productivity of single wells. The Khasib reservoirs in the AB oilfield show uniform vertical distributions, with a large difference in the longitudinal pore throats, and the upper part of the reservoir is better than the lower one. Because the hydrocarbon accumulation is multiphasic, the heterogeneity of crude oil is based on whether traps are formed in the early or late stage of source rock formation during accumulation. Moreover, the longitudinal physical differences in crude oil properties are mainly attributed to the longitudinal differences in the pore throats. The difference in the pore throat and physical properties of crude oil affords differences in the productivity of horizontal wells in different subzones.

Recently, two-dimensional magnetic materials (2DMMs) have become a focused research direction in a broad range of two-dimensional materials, due to their underlying significance in fundamental research, as well as in technologically relevant applications for future spintronics, magnonics, quantum information and data storage. The rich toolbox of 2DMMs and their diverse tunability have enabled the unprecedented research concerning the two-dimensional magnetic order down to single atomic layer of materials, much beyond the classical thin film magnetism, showing an extremely promising avenue towards electronics, magneto-optics and photonics. Among various degrees of freedoms, the interaction between spin and phonon (i.e., quanta of lattice vibration), hence the so-called spin-phonon coupling, serves as an important tuning knob to explore the two-dimensional magnetism, creates new types of quasiparticles and controls the magnetic order. This review overviews the latest developments in spin-phonon coupling investigations in 2DMMs. Various techniques utilizing spin-phonon coupling to investigate two-dimensional magnetism are discussed. Recent progress in tuning two-dimensional magnetic order based on spin-phonon coupling is also summarized, with a focus to understand the new functionalities. Furthermore, device developments and concepts based on spin-phonon coupling are briefly discussed. This review will provide our perspectives on the existing challenges and future directions in spin-phonon coupling research in 2DMMs and their functional devices.

During the joint extraction of entity and relationship from the operational management data of hydraulic engineering, complex sentences containing multiple triplets and overlapping entity relations often arise. However, traditional joint extraction models suffer from a single-feature representation approach, which hampers the effectiveness of entity relation extraction in complex sentences within hydraulic engineering datasets. To address this issue, this study proposes a multi-feature joint entity relation extraction method based on global context mechanism and graph convolutional neural networks. This method builds upon the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model and utilizes a bidirectional gated recurrent unit (BiGRU) and global context mechanism (GCM) to supplement the contextual and global features of sentences. Subsequently, a graph convolutional network (GCN) based on syntactic dependencies is employed to learn inter-word dependency features, enhancing the model's knowledge representation capabilities for complex sentences. Experimental results demonstrate the effectiveness of the proposed model in the joint extraction task on hydraulic engineering datasets. The precision, recall, and F1-score are 86.5%, 84.1%, and 85.3%, respectively, all outperforming the baseline model. [ABSTRACT FROM AUTHOR]

High-energy exciton emission could allow single-component multi-colour display or white light-emitting diodes. However, the thermal relaxation of high-energy excitons is much faster than the photon emission of them, making them non-emissive. Here, we report quantum dots with light hole-heavy hole splitting exhibiting strong high-energy exciton electroluminescence from high-lying light holes, opening a gate for high-performance multi-colour light sources. The high-energy electroluminescence can reach 44.5% of the band-edge heavy-hole exciton emission at an electron flux density Φe of 0.71 × 1019 s−1 cm−2 − 600 times lower than the photon flux density Φp (4.3 × 1021 s−1 cm−2) required for the similar ratio. Our simulation and experimental results suggest that the oscillator strength of heavy holes reduces more than that of light holes under electric fields. We attribute this as the main reason for strong light-hole electroluminescence. We observe this phenomenon in both CdxZn1-xSe-ZnS and CdSe-CdS core-shell quantum dots exhibiting large light hole-heavy hole splittings. Previous observations of high-energy exciton electroluminescence rely on filling the ground state with excessive charges, Wang et al. report such observation from quantum dots at low charge concentration by increasing the relative oscillator strength of higher-lying light holes comparing to that of ground-state heavy holes. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of extreme weather in recent years, accurate runoff prediction is crucial for the rational planning and management of water resources. Addressing the high uncertainty and multiple influencing factors in runoff prediction, this paper proposes a runoff prediction method driven by multi-source data. Based on multivariate observed data of runoff, water level, temperature, and precipitation, a Time2Vec-TCN-Transformer model is proposed for runoff prediction research and compared with LSTM, TCN, and TCN-Transformer models. The results show that the Time2Vec-TCN-Transformer model outperforms other models in metrics including MAE, RRMSE, MAPE, and NSE, demonstrating higher prediction accuracy and reliability. By effectively combining Time2Vec, TCN, and Transformer, the proposed model improves the MAPE for forecasting 1–4 days in the future by approximately 7% compared to the traditional LSTM model and 4% compared to the standalone TCN model, while maintaining NSE consistently between 0.9 and 1. This model can better capture the periodicity, long-term scale information, and relationships among multiple variables of runoff data, providing reliable predictive support for flood forecasting and water resources management. [ABSTRACT FROM AUTHOR]

The domain terms in power grid operation management documents are professional and complex. In the process of information extraction, excellent and applicable event detection methods are needed to extract event subjects. However, most of the current Chinese event detection methods use the word embedding technology to capture semantic representation, but it is difficult for these methods to capture the dependency between trigger words and other domain words in the same sentence. Based on the above situation, this paper proposes a novel hybrid representation architecture to represent the semantic and structural information of two characters and words. The model can capture rich semantic features through the semantic representation generated by the dependency parser. The experiment is based on the corpus of dispatching log, dispatching maintenance ticket, and dispatching plan. The results show that this method can significantly improve the performance of power grid dispatching text event detection.

To meet the demand of scalability and decision-making foresight of the traditional dispatching automation system in the new power system, a novel pre-dispatching method of new generation dispatching and control was proposed. First, the power grid digital twin was established in the description subsystem layer, which can reflect the state of power grid primary equipment, secondary equipment, and environment. Then, in the prediction subsystem level, the deep learning models were used to learn and predict future situation or accident risk of power grid operation in power grid digital twin. Finally, the feasibility of the proposed method was verified by the implementation example of East China Grid. The application results show that the pre-dispatching method improves the efficiency of system in dealing with the operation control problems of the new power system, which also provides a useful reference for comprehensive construction, popularization, and application of new generation power systems.

Diabetic kidney disease (DKD) is a leading factor in end-stage renal disease. The complexity of its pathogenesis, combined with the limited treatment efficacy, necessitates deeper insights into potential causes. Studies suggest that ferroptosis-driven renal tubular damage contributes to DKD's progression, making its counteraction a potential therapeutic strategy. Quercetin, a flavonoid found in numerous fruits and vegetables, has demonstrated DKD mitigation in mouse models, though its protective mechanism remains ambiguous. In this study, we delved into quercetin's potential anti-ferroptotic properties, employing a DKD rat model and high glucose (HG)-treated renal tubular epithelial cell models. Our findings revealed that HG prompted unusual ferroptosis activation in renal tubular epithelial cells. However, quercetin counteracted this by inhibiting ferroptosis and activating NFE2-related factor 2 (Nrf2) expression in both DKD rats and HG-treated HK-2 cells, indicating its renal protective role. Further experiments, both in vivo and in vitro, validated that quercetin stimulates Nrf2. Thus, our research underscores quercetin's potential in DKD treatment by modulating the ferroptosis process via activating Nrf2 in a distinct DKD rat model, offering a fresh perspective on quercetin's protective mechanisms. [ABSTRACT FROM AUTHOR]

[Objective] We attempted to determine the cause of the large difference in paleoenvironment characteristics during the Jurassic Da'anzhai period and the unclear distribution law of the shale source and reservoir in the Sichuan Basin. [Methods] Based on the geological drilling cores and field profiles of the Jurassic Da'anzhai member in the central and eastern Sichuan Basin, combined with elemental geochemical analysis methods, we performed a comparative analysis of the paleo-climate,paleo-redox conditions,and the paleo-salinity in the Da'anzhai period. [Results] The results show that the Da'anzhai member can be divided into shale, shell shale, mudstone, silty mudstone, argillaceous siltstone, shell limestone, and argillaceous shell limestone facies. The paleoenvironment of different sub-members in the Da'anzhai member is different to a certain extent. During the sedimentation period of the Third sub-member, the climate was dry to semi dry, and the water environment was fresh to semi-saline under sub-reducing conditions, located in a shallow to semi-deep lake environment. During the sedimentation of the Second submember, the climate was semi dry to semi humid, with more precipitation and lower temperatures, and the water environment was fresh water with sub-reducing conditions. It was a semi-deep lake environment. During the sedimentation of the First sub-member, the climate was dry to semi-dry, and the water environment was semi-saline to saline under oxidation sub-reduction conditions, located in a shallow lacustrine environment. Wells RA1 and YT1 both exhibited the following conditions: the climate was dry→wet→dry, the precipitation was less→more→less, the temperature was high→low→high, the reducibility of water was weak→strong→weak, and the paleo-salinity was high→low→high. From the sedimentation period of the Third sub-member to the early sedimentation period of the Second sub-member, the lacustrine basin depositional center migrated from the eastern to central Sichuan Basin. The middle sedimentation period of the Second sub-member was a transitional stage during which the migration direction of the lake basin depositional center changed. From the late sedimentation period of the Second sub-member to the sedimentation period of the First sub-member, the lacustrine basin depositional center migrated from the central to eastern Sichuan Basin. The average total organic carbon (TOC) of the Second sub-member is 1.70%, and the average porosity is 4.93%, significantly higher than the First and Third sub-members. The fluorescence display is strong, and it is the main source and reservoir of shale oil in the Da'anzhai section. The paleoenvironmental factors have a significant influence on the source and reservoir configuration. The more humid climate, more precipitation, lower temperature, stronger reducibility and lower salinity are conducive to shale deposition, organic matter enrichment, and pore development. [Conclusions] The study on the paleoenvironment of the Da'anzhai member in the central-eastern Sichuan Basin is important to the regularity of shale deposition, organic matter enrichment, and pore development degree. This research understanding can provide a theoretical basis for shale oil exploration in the Sichuan Basin. [ABSTRACT FROM AUTHOR]

Rationale: Pesticide isomers are widely available in agricultural production and may vary widely in biological activity, potency, and toxicity. Chromatographic and mass spectrometric analysis of pesticide isomers is challenging due to structural similarities. Methods: Based on liquid chromatography time‐of‐flight mass spectrometry, identification of cis–trans isomeric pesticides was achieved through retention time, characteristic fragment ions, and relative abundance ratio. Furthermore, theoretical and basic research has been conducted on the differences in characteristic fragment ions and their relative abundance ratios of cis–trans isomers. On the one hand, the cleavage pathways of six cis–trans isomers were elucidated through collision‐induced dissociation to explain different fragment ions of the isomers. On the other hand, for those with the same fragment ions but different abundance ratios, energy‐resolved mass spectrometry combined with computational chemical density functional theory in terms of kinetics, thermodynamics, and bond lengths was employed to explain the reasons for the differences in characteristic fragment ions and their abundance ratios. Results: A high‐resolution mass spectrometry method was developed for the separation and analysis of cis–trans isomers of pesticides in traditional Chinese medicine Radix Codonopsis, and six pesticide isomers were distinguished by retention time, product ions, and relative abundance ratios. The limits of quantification of the six pesticides were up to 10 μg/kg, and the linear ranges of them were 10–200 μg/kg, with coefficients of determination (R2) > 0.99, which demonstrated the good linearity of the six pesticides. The recoveries of the pesticides at spiked concentrations of 10, 20, and 100 μg/kg reached 70–120% with relative standard deviations ≤20%. Conclusions: It was demonstrated that the application of the method was well suited for accurate qualitative and quantitative analysis for isomers with different structures, which could avoid false‐negative results caused by ignoring other isomers effectively. [ABSTRACT FROM AUTHOR]

Enhancing the quality of junctions is crucial for optimizing carrier extraction and suppressing recombination in semiconductor devices. In recent years, metal halide perovskite has emerged as the most promising next-generation material for optoelectronic devices. However, the construction of high-quality perovskite junctions, as well as characterization and understanding of their carrier polarity and density, remains a challenge. In this study, using combined electrical and spectroscopic characterization techniques, we investigate the doping characteristics of perovskite films by remote molecules, which is corroborated by our theoretical simulations indicating Schottky defects consisting of double ions as effective charge dopants. Through a post-treatment process involving a combination of biammonium and monoammonium molecules, we create a surface layer of n-type low-dimensional perovskite. This surface layer forms a heterojunction with the underlying 3D perovskite film, resulting in a favorable doping profile that enhances carrier extraction. The fabricated device exhibits an outstanding open-circuit voltage (V OC) up to 1.34 V and achieves a certified efficiency of 19.31% for single-junction wide-bandgap (1.77 eV) perovskite solar cells, together with significantly enhanced operational stability, thanks to the improved separation of carriers. Furthermore, we demonstrate the potential of this wide-bandgap device by achieving a certified efficiency of 27.04% and a V OC of 2.12 V in a perovskite/perovskite tandem solar cell configuration. [ABSTRACT FROM AUTHOR]

Tight sandstone and shale oil and gas are the key targets of unconventional oil and gas exploration in the lake-delta sedimentary systems of China. Understanding the coevolutionary diagenesis of sandstone and shale reservoirs is crucial for the prediction of reservoir quality, ahead of drilling, in such systems. Thin-section description, scanning electron microscopy (SEM), X-ray diffraction (XRD), fluid inclusion analysis, porosity and permeability tests, high-pressure mercury intrusion (HPMI) measurements and nuclear magnetic resonance tests (NMR) were used to reveal the coevolutionary diagenetic mechanisms of a sandstone and shale reservoir in the Lianggaoshan Formation of the Eastern Sichuan Basin, China. The thermally mature, organic-matter-rich, dark shale of layer3 is the most important source rock within the Lianggaoshan Formation. It started to generate abundant organic acids at the early stage of mesodiagenesis and produced abundant hydrocarbons in the early Cretaceous. Porewater with high concentrations of Ca2+ and CO32− entered the sandstone reservoir from dark shale as the shale was compacted during burial. Potassium feldspar dissolution at the boundary of the sandstone was more pervasive than at the center of the sandstone. The K+ released by potassium feldspar dissolution migrated from the sandstone into mudstone. Grain-rimming chlorite coats occurred mainly in the center of the sandstone. Some silica exported from the shale was imported by the sandstone boundary and precipitated close to the shale/sandstone boundary. Some intergranular dissolution pores and intercrystal pores were formed in the shale due to dissolution during the early stages of mesodiagenesis. Chlorite coats, which precipitated during eodiagenesis, were beneficial to the protection of primary pore space in the sandstone. Calcite cement, which preferentially precipitated at the boundary of sandstone, was not conducive to reservoir development. Dissolution mainly occurred at the early stage of mesodiagenesis due to organic acids derived from the dark shale. Calcite cement could also protect some primary pores from compaction and release pore space following dissolution. The porosity of sandstone and shale was mainly controlled by the thickness of sandstone and dark shale. [ABSTRACT FROM AUTHOR]

Objective: Microglial polarization plays a critical role in neuroinflammation and may be a potential therapeutic target for ischemic stroke. This study was to explore the role and underlying molecular mechanism of Circular RNA PTP4A2 (circPTP4A2) in microglial polarization after ischemic stroke. Methods: C57BL/6J mice underwent transient middle cerebral artery occlusion (tMCAO), while primary mouse microglia and BV2 microglial cells experienced oxygen glucose deprivation/reperfusion (OGD/R) to mimic ischemic conditions. CircPTP4A2 shRNA lentivirus and Colivelin were used to knock down circPTP4A2 and upregulate signal transducer and activator of transcription 3 (STAT3) phosphorylation, respectively. Microglial polarization was assessed using immunofluorescence staining and Western blot. RNA pull‐down and RNA binding protein immunoprecipitation (RIP) were applied to detect the binding between circPTP4A2 and STAT3. Results: The levels of circPTP4A2 were significantly increased in plasma and peri‐infarct cortex in tMCAO mice. CircPTP4A2 knockdown reduced infarct volume, increased cortical cerebral blood flow (CBF), and attenuated neurological deficits. It also decreased pro‐inflammatory factors levels in peri‐infarct cortex and plasma, and increased anti‐inflammatory factors concentrations 24 h post‐stroke. In addition, circPTP4A2 knockdown suppressed M1 microglial polarization and promoted M2 microglial polarization in both tMCAO mice and OGD/R‐induced BV2 microglial cells. Moreover, circPTP4A2 knockdown inhibited the phosphorylation of STAT3 induced by oxygen–glucose deprivation. In contrast, increased phosphorylation of STAT3 partly counteracted the effects of circPTP4A2 knockdown. RNA pull‐down and RIP assays further certified the binding between circPTP4A2 and STAT3. Conclusion: These results revealed regulatory mechanisms of circPTP4A2 that stimulated neuroinflammation by driving STAT3‐dependent microglial polarization in ischemic brain injury. CircPTP4A2 knockdown reduced cerebral ischemic injury and promoted microglial M2 polarization, which could be a novel therapeutic target for ischemic stroke. [ABSTRACT FROM AUTHOR]

Objective: Neuronal precursor cells expressed developmentally down‐regulated 4 (Nedd4) are believed to play a critical role in promoting the degradation of substrate proteins and are involved in numerous biological processes. However, the role of Nedd4 in intracerebral hemorrhage (ICH) remains unknown. This study aims to investigate the regulatory role of Nedd4 in the ICH model. Methods: Male C57BL/6J mice were induced with ICH. Subsequently, the levels of glutathione peroxidase 4 (GPX4), malondialdehyde (MDA) concentration, iron content, mitochondrial morphology, as well as the expression of divalent metal transporter 1 (DMT1) and Nedd4 were assessed after ICH. Furthermore, the impact of Nedd4 overexpression was evaluated through analyses of hematoma area, ferroptosis, and neurobehavioral function. The mechanism underlying Nedd4‐mediated degradation of DMT1 was elecidated using immunoprecipitation (IP) after ICH. Results: Upon ICH, the level of DMT1 in the brain increased, but decreased when Nedd4 was overexpressed using Lentivirus, suggesting a negative correlation between Nedd4 and DMT1. Additionally, the degradation of DMT1 was inhibited after ICH. Furthermore, it was found that Nedd4 can interact with and ubiquitinate DMT1 at lysine residues 6, 69, and 277, facilitating the degradation of DMT1. Functional analysis indicated that overexpression of Nedd4 can alleviate ferroptosis and promote recovery following ICH. Conclusion: The results demonstrated that ferroptosis occurs via the Nedd4/DMT1 pathway during ICH, suggesting it potential as a valuable target to inhibit ferroptosis for the treatment of ICH. [ABSTRACT FROM AUTHOR]