Scientific Reports最新文献

{"title":"Morphologic and functional alterations in the parasagittal dural space in mild cognitive impairment.","authors":"Bio Joo, Mina Park, Song Soo Kim, Sung Jun Ahn, Han-Kyeol Kim, Hanna Cho, Chul Hyoung Lyoo, Sang Hyun Suh","doi":"10.1038/s41598-025-07909-3","DOIUrl":"https://doi.org/10.1038/s41598-025-07909-3","url":null,"abstract":"<p><p>This study aimed to identify morphologic and functional differences in the parasagittal dural space (PSD) between patients with mild cognitive impairment (MCI) and cognitively unimpaired participants using dynamic contrast-enhanced MRI (DCE MRI). A total of 29 MCI patients and nine controls underwent structural MRI and DCE MRI, where PSD volume and parameters such as peak wash-in rate and time to first peak enhancement were assessed. MCI patients had significantly larger PSD volume (P = 0.023), a lower peak wash-in rate (P < 0.001), and delayed time to first peak (P = 0.001) compared to controls. In multivariate regression analysis, PSD volume (β = -0.579, 95% CI [-1.072 - -0.086], P = 0.023) and wash-out rate (β = -3.293, 95% CI [-6.351 - -0.235], P = 0.036) were significantly associated with the Mini-Mental State Examination (MMSE) score. Additionally, a lower peak wash-in rate correlated significantly with lower cognitive performance, as measured by the Montreal Cognitive Assessment (MoCA) (P = 0.043). This association highlights a potential link between meningeal lymphatic dysfunction and cognitive decline, suggesting that PSD alterations could reflect early stages of neurodegenerative changes. In conclusion, these PSD structural and functional alterations in MCI patients may serve as early imaging markers, helping in the assessment of disease severity in neurodegenerative conditions such as Alzheimer's disease. This insight into meningeal lymphatic dysfunction provides a promising direction for early diagnosis and monitoring of cognitive impairment.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23882"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi class aerial image classification in UAV networks employing Snake Optimization Algorithm with Deep Learning.","authors":"Alanoud Al Mazroa, Nuha Alruwais, Muhammad Kashif Saeed, Kamal M Othman, Randa Allafi, Ahmed S Salama","doi":"10.1038/s41598-025-04570-8","DOIUrl":"https://doi.org/10.1038/s41598-025-04570-8","url":null,"abstract":"<p><p>In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. Deep Learning (DL), a powerful tool in artificial intelligence (AI), proves significant in this context, enabling the model to analyze and classify complex aerial images effectually. By utilizing advanced neural network architectures, such as convolutional neural networks (CNN), DL models outperform at identifying complex features and patterns within the aerial imagery. These models can extract spectral and spatial information from the captured data, classifying diverse terrains, structures, and objects precisely. Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. As UAV networks continue to expand, DL-powered multi-class AIC significantly enhances the performance of surveillance, reconnaissance, and remote sensing tasks, contributing to the advancement of autonomous aerial systems. This study proposes a Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC) methodology on UAV Networks. The main purpose of SOADL-MCAIC methodology is to recognize the presence of multiple classes of aerial images on the UAV networks. To accomplish this, the SOADL-MCAIC technique utilizes Gaussian filtering (GF) for pre-processing. In addition, the SOADL-MCAIC technique employs the Efficient DenseNet model to learn difficult and intrinsic features in the image. The SOA-based hyperparameter tuning process is used to enhance the performance of the Efficient DenseNet technique. Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. The simulation outcomes of the SOADL-MCAIC method are examined under the UCM land use dataset. The experimental analysis of the SOADL-MCAIC method portrayed a superior accuracy value of 99.75% over existing models.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23872"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.","authors":"Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang","doi":"10.1038/s41598-025-09010-1","DOIUrl":"https://doi.org/10.1038/s41598-025-09010-1","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment essential. In this study, we utilized large-scale gene expression data from TCGA and GSE54236, alongside eQTL GWAS data, to identify key genes that influence HCC prognosis. Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. The prognostic performance of model was validated through survival analysis, and its association with chemotherapy and immunotherapy sensitivity. The impact of key genes on the proliferation and invasion capabilities of HCC cells was assessed through Western blot (WB), EdU, and invasion assays. A total of 27 candidate genes associated with HCC survival were identified, with 16 genes categorized as high-risk. The riskScore model demonstrated excellent performance in stratifying patients into high-risk and low-risk groups, with C-index exceeding 0.7 for both TCGA and GSE54236 datasets. High-risk patients exhibited poorer prognosis and higher immune cell infiltration, particularly T cells and neutrophils. The model also predicted drug sensitivity, with high-risk patients showing greater sensitivity to chemotherapy agents like 5-Fluorouracil and Paclitaxel. Mutation analysis revealed that TP53 and MUC16 mutations were prevalent in high-risk groups, highlighting their role in HCC progression and therapeutic response. And the key gene SLC16A3 and STRBP can significantly promote the proliferation and invasion ability of HCC cells. Our riskScore model, integrating genetic and immune factors, provides a robust prognostic tool with potential clinical application in patient stratification and chemotherapy decision-making for HCC patients.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23930"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis.","authors":"Luis B Elvas, Rafaela Santos, João C Ferreira","doi":"10.1038/s41598-025-05695-6","DOIUrl":"https://doi.org/10.1038/s41598-025-05695-6","url":null,"abstract":"<p><p>The analysis of medical imaging reports is labour-intensive but crucial for accurate diagnosis and effective patient screening. Often presented as unstructured text, these reports require systematic organisation for efficient interpretation. This study applies Natural Language Processing (NLP) techniques tailored for European Portuguese to automate the analysis of cardiology reports, streamlining patient screening. Using a methodology involving tokenization, part-of-speech tagging and manual annotation, the MediAlbertina PT-PT language model was fine-tuned, achieving 96.13% accuracy in entity recognition. The system enables rapid identification of conditions such as aortic stenosis through an interactive interface, substantially reducing the time and effort required for manual review. It also facilitates patient monitoring and disease quantification, optimising healthcare resource allocation. This research highlights the potential of NLP tools in Portuguese healthcare contexts, demonstrating their applicability to medical report analysis and their broader relevance in improving efficiency and decision-making in diverse clinical environments.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23949"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Upregulation of VEGFA through the adenosine A2A receptor is a crucial pathway for inhibiting pericyte apoptosis in chronic cerebral hypoperfusion.","authors":"Deyue Li, Pan Gao, Wei Duan","doi":"10.1038/s41598-025-08407-2","DOIUrl":"https://doi.org/10.1038/s41598-025-08407-2","url":null,"abstract":"<p><p>Chronic cerebral hypoperfusion (CCH) is a key factor in vascular cognitive impairment. Pericyte loss and subsequent blood-brain barrier disruption play pivotal roles in the pathogenesis of CCH-induced white matter lesions (CCH-WMLs). Previous work suggested that the adenosine A2A receptor (A2AR) may protect pericytes in CCH-WMLs, but the mechanisms are not fully understood. In this study, we induced CCH in Sprague‒Dawley rats via bilateral carotid artery occlusion and treated them with the A2AR agonist CGS21680 or the A2AR antagonist SCH58261. Our findings revealed that CGS21680 significantly inhibited the expression of the proapoptotic proteins BAX and Caspase 3, while SCH58261 obviously promoted it. The expression of the antiapoptotic protein Bcl-2 was markedly increased by CGS21680 in OGD-exposed pericytes. Additionally, the expression of the transcription factors Rap-1, ERK, and phosphorylated ERK also increased dramatically in OGD-exposed pericytes following CGS21680 administration. VEGFA and VEGFR2 expression was upregulated by CGS21680 and downregulated by SCH58261 in pericytes after OGD. Furthermore, VEGFA knockdown via a shRNA-expressing adenovirus counteracted the protective effect of A2AR against pericyte apoptosis following OGD. Notably, the expression of BAX and Caspase3 was significantly upregulated, and the expression of BCL-2 was markedly downregulated in OGD-exposed pericytes after Rap-1 knockdown via a shRNA-expressing adenovirus. Rap-1 suppression obviously reduced the levels of phosphorylated ERK, VEGFA and VEGFR2 in pericytes, suggesting a role for the Rap1-ERK pathway in the A2AR-induced upregulation of VEGFA expression. Overall, A2AR activation inhibits pericyte apoptosis and may exert neuroprotective effects against CCH by increasing VEGFA expression through the Rap1-ERK signaling pathway.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23955"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-strategy enhanced artificial rabbits optimization for prediction of grades in tourism service communication courses.","authors":"Xiaodan Qu, Zhuyin Jia","doi":"10.1038/s41598-024-84931-x","DOIUrl":"https://doi.org/10.1038/s41598-024-84931-x","url":null,"abstract":"<p><p>Predicting students' grades through their classroom behavior has been a longstanding concern in education. Recently, artificial intelligence has demonstrated remarkable potential in this area. In this study, the Artificial Rabbits Optimization Algorithm is selected to enhance the predictor's capabilities. This algorithm, a recently proposed and popular metaheuristic method, is known for its simple and straightforward structure. However, like other metaheuristic algorithms, it often falls into local optima, and as iterations increase, the convergence speed slows down, leading to reduced convergence accuracy. To address this issue, a Multi-Strategy Enhanced Artificial Rabbits Optimization Algorithm (MEARO) is introduced. MEARO first employs a Nonlinear Exploration and Exploitation Transition Factor (NL) to improve the balance between exploration and exploitation. Additionally, a Stochastic Centroid Backward Learning approach (SOBL) is applied to enhance both the quality and diversity of the population, ensuring a broader optimization of the search area and increasing the chances of locating the global optimum. Finally, a Dynamic Changing Step Length Development strategy is incorporated to enhance the randomness and development capability. The efficiency of MEARO is confirmed by comparing its performance with eight other sophisticated algorithms using the CEC2017 benchmark. The number of wins/Ties/losses in the three dimensions of cec2017 are (223/0/17), (221/0/19) and (230/0/10) respectively. Results indicate that MEARO outperforms these algorithms. Furthermore, MEARO is used to optimize two critical parameters of the Kernel Extreme Learning Machine (KELM), significantly improving its classification performance. Experimental results on the collected student performance dataset demonstrate that the KELM model optimized by MEARO surpasses other benchmark models across various metrics. Additionally, factors such as interest in the course, frequency of classroom discussion, and access to supplementary knowledge and information related to the course are identified as significant contributors to performance.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23854"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacovigilance analysis of drug-induced hypertrophic rhinitis using FAERS data.","authors":"Yan He, Xinzhou Yan, Long Chen, Zhiqiang Lin","doi":"10.1038/s41598-025-10336-z","DOIUrl":"https://doi.org/10.1038/s41598-025-10336-z","url":null,"abstract":"<p><p>Our study aims to evaluate the risk of drug-induced hypertrophic rhinitis and analyze its epidemiological characteristics utilizing real-world data. We employed reporting odds ratios (ROR) to assess the disproportionality in reports of drug-induced hypertrophic rhinitis between January 2004 and September 2024. Single-factor, LASSO, and multi-factor regression analyses were conducted to investigate drugs associated with hypertrophic rhinitis. The Bonferroni correction was implemented to conduct multiple comparisons. 250 drugs were linked to hypertrophic rhinitis, with 85 drugs (case number > 100) identified as independent risk factors for drug-induced hypertrophic rhinitis. Those drugs' indications were classified as Allergic disease (19/85), Multi-indication (13/85), Cardiovascular disease (12/85), Rheumatoid disease (6/85), Autoimmune disease (6/85), Respiratory disease (5/85), Cancer (4/85), Metabolic disease (4/85), Contrast agent (3/85), Erectile dysfunction (3/85), Infection (3/85), Disease of the nervous system (2/85), Urologic Disease (2/85) Esophageal disease (1/85), Hereditary disorder (1/85), and Ophthalmic disease (1/85). Multiple medications have possible risks of drug-induced hypertrophic rhinitis. Further research is needed to clarify causality and guide clinical decision-making.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23937"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of mineral composition and trace metal cations on the body color of South African Sugilite Jade.","authors":"Pengyu Li, Ying Guo","doi":"10.1038/s41598-025-09281-8","DOIUrl":"https://doi.org/10.1038/s41598-025-09281-8","url":null,"abstract":"<p><p>Sugilite jade is an emerging jade material in the jewelry market with various shades of purple in appearance, and there is currently a gap in research on the blue and pink hues it possesses. The mineralogical and colorimetric characteristics of South African sugilite jade were studied using polarized light microscopy, X-ray fluorescence, X-ray powder diffraction, electron microprobe, infrared spectroscopy, and UV-Vis spectroscopy to explore the causes of its color. The primary mineralogical component of sugilite jade is sugilite, with minor constituents including pectolite-serandite, aegirine, alkaline amphibole, and alkaline feldspar. The purple hue of sugilite jade is attributed to the presence of Mn³⁺ on the A site within the crystal structure of sugilite. It has been observed that the lightness and chroma of this color are positively correlated with the Mn content. The blue color is caused by Cu²⁺ in pectolite and the pink color is attributed to Mn²⁺ in serandite. The color of sugilite is primarily influenced by its broad absorption peak in the UV-visible spectrum, which occurs between 500 and 700 nm. The intensity of this peak is directly proportional to the concentration of Mn³⁺, which determines the lightness of sugilite jade. The presence of Cu²⁺ results in a shift of the absorption peak to a higher wavelength, imparting a bluer color to the mineral.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23887"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thread design optimization of a dental implant using explicit dynamics finite element analysis.","authors":"Qi Zhong, Zidi Zhai, Zi'ang Wu, Yingyi Shen, Fang Qu, Yaqin Wu, Ziyuan Zhu, Chun Xu","doi":"10.1038/s41598-025-08858-7","DOIUrl":"https://doi.org/10.1038/s41598-025-08858-7","url":null,"abstract":"<p><p>To optimize the thread design of a commercial dental implant for ideal stress distribution in the peri-implant bone. The models of the BLT^® Φ4.1 × 10 implant (Institut Straumann AG) and the alveolar bone were created. Dynamic von Mises stress (σ_vM) in the peri-implant cortical bone during and immediately after implantation was calculated using explicit dynamics finite element analysis (EDFEA). The combination of thread pitch, depth, tip width (TW), and coronal/apical surface angle (CSA/ASA), which produced minimal σ_vM was determined as the optimal thread design by orthogonal experimental design. The implants with optimal and original thread designs were fabricated and implanted into rabbits' tibias. Implant stability quotient (ISQ), bone-to-implant contact (BIC), and bone volume fraction in 500 μm (BV/TV-500) and 1000 μm range (BV/TV-1000) were measured to evaluate the osseointegration performance of the implants. The implant thread design of 0.8-mm pitch, 0.2-mm depth, 0.15-mm TW, 10-degree CSA, and 10-degree ASA produced minimal σ_vM for the maxillary posterior region (OPT-max). The thread design of 1.0-mm pitch, 0.3-mm depth, 0.2-mm TW, 0-degree CSA, and 20-degree ASA produced minimal σ_vM for the mandibular posterior region (OPT-man). Optimized implants showed significantly improved ISQ value (p < 0.05) 4 weeks after implantation. The BV/TV-500 and BV/TV-1000 around the OPT-max, and the BIC and BV/TV-500 around the OPT-man implant were significantly higher than those around the originally designed implant, respectively (p < 0.05). The thread design significantly affects the stress in the peri-implant bone during and immediately after the implantation. The optimal thread design based on EDFEA promoted the osteogenesis around the implant.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23868"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aboveground net primary production spatio-temporal changes in the bioclimates of Alborz mountains based on multi-sensor satellite data.","authors":"Marzieh Asgari, Reza Jafari, Mostafa Tarkesh Esfahani, Mahshid Souri","doi":"10.1038/s41598-025-08923-1","DOIUrl":"https://doi.org/10.1038/s41598-025-08923-1","url":null,"abstract":"<p><p>Aboveground Net Primary Production (ANPP) is a key indicator for assessing the health of rangeland ecosystems. This study estimated ANPP in the central Alborz rangelands of Iran from 2000 to 2020 based on satellite data (MOD13Q1.061, Sentinel-2 L1C), ground-based measurements, along with meteorological data through the Carnegie Ames Stanford Approach model. ANPP trends were ascertained across various bioclimates and vegetation types. A total of 240 sampling sites were selected to measure ANPP using a systematic-random design. Sentinel-2 ANPP values ranged between 2.4 and 44.6 gC/m² in the study area. The model evaluation, based on the coefficient of determination, indicated a strong relationship between Sentinel-2 derived ANPP and ground data (R² = 0.86, P < 0.01). A significant relationship was also observed between ANPP estimates from the Sentinel-2 and MODIS sensors (R² = 0.8, P < 0.01). The climatic conditions and type of vegetation have a significant impact on rangeland production. The highest annual average ANPP, estimated using MODIS, was 60.57 gC/m², witnessed in the Psathyrostachys fragilis-Agropyron tauri vegetation type within a humid and cold climate. In contrast, the lowest ANPP, 39.07 gC/m², was recorded for the Seidlitzia rosmarinus-Artemisia sieberi type in a hyper-arid and cold climate. Generally, the findings demonstrated that integrating modeling approaches with satellite imagery enables robust estimation and analysis of rangeland production dynamics across diverse bioclimates and vegetation types.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"23903"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}