Tailings pedogenesis plays a fundamental role in the ecological restoration of mining wastelands by converting barren tailings into soil-like substrates through physical,chemical,and biological processes.To systematic...Tailings pedogenesis plays a fundamental role in the ecological restoration of mining wastelands by converting barren tailings into soil-like substrates through physical,chemical,and biological processes.To systematically investigate the contributions and interactions of natural weathering and plant regeneration in the tailings pedogenesis,this study analyzed the microstructure,chemical composition,and rhizosphere microbial communities of original tailings samples(OR),15-year naturally weathered samples(PW),and naturally regenerated samples spontaneously colonized by Miscanthus(PM),Lolium perenne(LP),and Cynodon dactylon(CD).X-ray micro-computed tomography revealed that natural weathering increased the total soil porosity of the tailings by 13.45%,with negligible effects on chemical properties.After natural regeneration,soil porosity further increased from 18.74%to 41.45%.Scanning Electron Microscope revealed microaggregates attaching to the root surfaces.In addition,plant species exhibited distinct influences on soil chemical properties.Specifically,PM significantly increased soil organic matter and nitrate nitrogen content,whereas CD primarily promoted the accumulation of rapidly available potassium.Compared to the OR,natural weathering initiated the reconstruction of microbial communities,which were further enriched by plant root systems during natural regeneration.Notably,PM enriched functional genera such as Haliangium and Bryobacter,which were positively associated with heavy metal stabilization,suggesting its role as a critical pioneer species for ecological restoration of tailings.This study highlights the distinct and synergistic roles of natural weathering and plant regeneration in tailings pedogenesis,offering insights for plant selection and ecological restoration strategies.展开更多
Objective To examine national trends in antibiotic consumption and antimicrobial resistance(AMR)among six WHO-priority bacterial pathogens in China from 2016 to 2022.Methods This ecological study analyzed national and...Objective To examine national trends in antibiotic consumption and antimicrobial resistance(AMR)among six WHO-priority bacterial pathogens in China from 2016 to 2022.Methods This ecological study analyzed national and provincial data from the China Antibacterial Resistance Surveillance System(CARSS)and the National Hospital Information Network.Beta regression models assessed temporal trends,and hierarchical models evaluated associations between antibiotic use and resistance.Results From 2016 to 2022,carbapenem resistance in Acinetobacter baumannii and Pseudomonas aeruginosa,and vancomycin resistance in Enterococcus faecium and E.faecalis significantly declined(β<0,P<0.010),while carbapenem-resistant Klebsiella pneumoniae increased(β=0.081,P<0.001).Nationwide antibiotic consumption rose across 10 major classes.Positive associations were found between carbapenem use and resistance in A.baumannii(z=2.719,P=0.007)and P.aeruginosa(z=3.241,P=0.001),and between vancomycin use and resistance in E.faecium(z=4.510,P=0.001)and E.faecalis(z=3.210,P=0.001).Conclusion Carbapenem-resistant K.pneumoniae increased significantly in China,while other resistant pathogens declined.Resistance patterns were linked to the use of multiple antibiotic classes,underscoring the need for strengthened antibiotic stewardship and surveillance.展开更多
Early prevention and control of coal spontaneous combustion have emerged as a critical research area in coal mine safety.Due to their sustainability and environmental friendliness,microorganisms have gained attention....Early prevention and control of coal spontaneous combustion have emerged as a critical research area in coal mine safety.Due to their sustainability and environmental friendliness,microorganisms have gained attention.A filamentous fungus was collected in the coal mine and identified as Absidia spinosa.Results indicated that the mycelium effectively covered and repaired many coal pores.The oxygen consumption ratio of A.spinosa was higher in coal-containing environments than in coal-free conditions.The fungus significantly impacted aliphatic functional groups,disrupting bridging bonds and side chains connected to aromatic structures and reducing the relative content of C—O bonds.Additionally,A.spinosa increases the ignition temperature by 25.34℃.The total heat release was decreased by approximately 32.58%,and the activation energies were increased.The genome of Absidia spinosa revealed genes related to oxygen consumption,small molecule degradation,and secretion of metabolic products,such as those annotated under GO ID:0140657,etc.The pathways involved in the degradation of small organic molecules(e.g.,ko00626,etc.),carbon fixation,and nitrogen cycling,all linked to coal decomposition.Through oxygen consumption and the alteration of coal-active structures,A.spinosa effectively inhibits CSC,providing an experimental basis for exploring eco-friendly biological control methods in the goaf.展开更多
Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of eco...Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.展开更多
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)...Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments.展开更多
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a class...Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area.展开更多
BACKGROUND The classification of ketosis-onset diabetes is controversial,and the prevalence and clinical characteristics of diabetic retinopathy(DR)in ketosis-onset diabetes mellitus remain unclear.AIM To compare the ...BACKGROUND The classification of ketosis-onset diabetes is controversial,and the prevalence and clinical characteristics of diabetic retinopathy(DR)in ketosis-onset diabetes mellitus remain unclear.AIM To compare the prevalence and risk factors of DR among patients with type 1 diabetes mellitus(T1DM),ketosis-onset diabetes,and non-ketotic type 2 diabetes mellitus(T2DM).METHODS This real-world observational study enrolled 1777 patients with newly diagnosed diabetes between January 2003 and December 2012,including 211 with T1DM,673 with ketosis-onset diabetes,and 893 with non-ketotic T2DM.DR was assessed using digital nonmydriatic fundus photography,and its severity was graded based on the ETDRS classification.Clinical characteristics and risk factors of DR were compared across the three groups.RESULTS After controlling for age and sex,DR prevalence was significantly higher in ketosis-onset diabetes(9.5%)than in T1DM(5.7%,P=0.034),but not significantly different from non-ketotic T2DM(12.3%,P=0.105).Risk factors for DR in ketosis-onset diabetes and non-ketotic T2DM included increased estimated glomerular filtration rate(eGFR)and urinary albumin excretion.In contrast,elevated eGFR and 2-hour postprandial C-peptide were independent risk factors for DR in T1DM.The prevalence and risk factors for DR in ketosis-onset diabetes were similar to those in non-ketotic T2DM,but differed from T1DM.CONCLUSION These findings further support classifying ketosis-onset diabetes as a subtype of T2DM rather than idiopathic T1DM.However,the distinctive clinical features of ketosis-onset diabetes should not be ignored.展开更多
为解决设施蔬菜连作障碍严重问题,明确不同轮作模式对设施蔬菜土壤微生物群落结构和功能的影响,本研究基于宏基因组测序技术,对生菜-水稻(S1)、生菜-旱稻(S2)、生菜-油麦菜(S3)3种轮作模式下的微生物群落结构和多样性、互作网络和群落...为解决设施蔬菜连作障碍严重问题,明确不同轮作模式对设施蔬菜土壤微生物群落结构和功能的影响,本研究基于宏基因组测序技术,对生菜-水稻(S1)、生菜-旱稻(S2)、生菜-油麦菜(S3)3种轮作模式下的微生物群落结构和多样性、互作网络和群落功能变化等进行研究。结果表明,相较于生菜连作(CK),3种轮作模式均在不同程度上提高了土壤细菌群落α多样性,生菜-水稻轮作模式下Chao1和ACE指数增幅最大,Simpson指数降幅最大,Shannon指数在生菜-旱稻轮作模式下增幅最大;主坐标分析(PCoA)和置换多元方差分析(PERMANOVA)表明,3种轮作模式显著改变了土壤细菌群落组成,慢生根瘤菌属(Bradyrhizobium)、Gp6-AA40、PSRF01、VBCG01在3种轮作模式下均显著富集,且慢生根瘤菌属和PSRF01在生菜-水稻轮作处理下相对丰度最高;共现网络分析表明,轮作模式不仅增强了微生物网络的复杂性与稳定性,而且改变了微生物关键类群,生菜-水稻轮作模式关键类群为PSRF01、慢生根瘤菌属、鞘氨醇单胞菌属(Sphingomicrobium)、溶杆菌属(Lysobacter)等有益细菌,生菜-旱稻轮作模式关键类群为硝化螺旋菌属(Nitrospira)和鞘氨醇单胞菌属等,生菜-油麦菜轮作模式关键类群为硝化螺旋菌属和溶杆菌属等;基于KEGG(Kyoto Encyclopedia of Genes and Genomes)数据库进行功能预测,3种轮作模式下共有17个子功能基因(KEGG途径)的相对丰度显著富集,其中,膜转运、运输和分解代谢、环境适应性、糖代谢、能量代谢等12条代谢途径相关的功能基因丰度在生菜-水稻轮作模式下增幅最大。综上所述,3种轮作模式皆改变了设施蔬菜土壤微生物群落组成、多样性和功能,其中,水旱轮作模式(生菜-水稻轮作)各方面指标的效果均最佳。因此,缓解或避免设施生菜连作障碍理想的轮作模式为生菜-水稻轮作,生菜-旱稻轮作次之。展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages...We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages.The experimental results show that the performance and self-healing effect of composite micro-organisms are significantly better than those of single microorganisms.For cracks with widths of 0.2-0.4 mm,the repair effect of the composite microorganisms at 28 days is 42.7%and 71.2%higher than that of pure Bacillus and pure yeast,respectively.The repairing rate of the area with the widths of the cracks of 0.2-0.4,0.4-0.6,and 0.6-0.8 mm are 100%,77.3%,and 53.4%,respectively.The area repair rates corresponding to cracking ages of 56,90,and 180 days are 73.3%,55.4%,and 30.8%,respectively.展开更多
基金supported by the National Key R&D Program of China(No.2023YFE0114500)the Natural Science Foundation of China(No.42477134).
摘要Tailings pedogenesis plays a fundamental role in the ecological restoration of mining wastelands by converting barren tailings into soil-like substrates through physical,chemical,and biological processes.To systematically investigate the contributions and interactions of natural weathering and plant regeneration in the tailings pedogenesis,this study analyzed the microstructure,chemical composition,and rhizosphere microbial communities of original tailings samples(OR),15-year naturally weathered samples(PW),and naturally regenerated samples spontaneously colonized by Miscanthus(PM),Lolium perenne(LP),and Cynodon dactylon(CD).X-ray micro-computed tomography revealed that natural weathering increased the total soil porosity of the tailings by 13.45%,with negligible effects on chemical properties.After natural regeneration,soil porosity further increased from 18.74%to 41.45%.Scanning Electron Microscope revealed microaggregates attaching to the root surfaces.In addition,plant species exhibited distinct influences on soil chemical properties.Specifically,PM significantly increased soil organic matter and nitrate nitrogen content,whereas CD primarily promoted the accumulation of rapidly available potassium.Compared to the OR,natural weathering initiated the reconstruction of microbial communities,which were further enriched by plant root systems during natural regeneration.Notably,PM enriched functional genera such as Haliangium and Bryobacter,which were positively associated with heavy metal stabilization,suggesting its role as a critical pioneer species for ecological restoration of tailings.This study highlights the distinct and synergistic roles of natural weathering and plant regeneration in tailings pedogenesis,offering insights for plant selection and ecological restoration strategies.
基金supported by the Beijing Natural Science Foundation(L242149)Research Project on High Quality Development of Hospital Pharmacy,National Institute of Hospital Administration,NHC,China(NIHAYS2332)+1 种基金National High Level Hospital Clinical Research Funding(BJ-2023-199)Capital Funds for Health Improvement and Research(CFH)(2024-1-4052).
摘要Objective To examine national trends in antibiotic consumption and antimicrobial resistance(AMR)among six WHO-priority bacterial pathogens in China from 2016 to 2022.Methods This ecological study analyzed national and provincial data from the China Antibacterial Resistance Surveillance System(CARSS)and the National Hospital Information Network.Beta regression models assessed temporal trends,and hierarchical models evaluated associations between antibiotic use and resistance.Results From 2016 to 2022,carbapenem resistance in Acinetobacter baumannii and Pseudomonas aeruginosa,and vancomycin resistance in Enterococcus faecium and E.faecalis significantly declined(β<0,P<0.010),while carbapenem-resistant Klebsiella pneumoniae increased(β=0.081,P<0.001).Nationwide antibiotic consumption rose across 10 major classes.Positive associations were found between carbapenem use and resistance in A.baumannii(z=2.719,P=0.007)and P.aeruginosa(z=3.241,P=0.001),and between vancomycin use and resistance in E.faecium(z=4.510,P=0.001)and E.faecalis(z=3.210,P=0.001).Conclusion Carbapenem-resistant K.pneumoniae increased significantly in China,while other resistant pathogens declined.Resistance patterns were linked to the use of multiple antibiotic classes,underscoring the need for strengthened antibiotic stewardship and surveillance.
基金supported by the National Natural Science Foundation of China(No.51974128)the National Key Research and Development Program of China(No.2023YFC3009105)。
摘要Early prevention and control of coal spontaneous combustion have emerged as a critical research area in coal mine safety.Due to their sustainability and environmental friendliness,microorganisms have gained attention.A filamentous fungus was collected in the coal mine and identified as Absidia spinosa.Results indicated that the mycelium effectively covered and repaired many coal pores.The oxygen consumption ratio of A.spinosa was higher in coal-containing environments than in coal-free conditions.The fungus significantly impacted aliphatic functional groups,disrupting bridging bonds and side chains connected to aromatic structures and reducing the relative content of C—O bonds.Additionally,A.spinosa increases the ignition temperature by 25.34℃.The total heat release was decreased by approximately 32.58%,and the activation energies were increased.The genome of Absidia spinosa revealed genes related to oxygen consumption,small molecule degradation,and secretion of metabolic products,such as those annotated under GO ID:0140657,etc.The pathways involved in the degradation of small organic molecules(e.g.,ko00626,etc.),carbon fixation,and nitrogen cycling,all linked to coal decomposition.Through oxygen consumption and the alteration of coal-active structures,A.spinosa effectively inhibits CSC,providing an experimental basis for exploring eco-friendly biological control methods in the goaf.
基金supported by the National Natural Science Foundation of China(U2344201 and 42101316)the Natural Science Foundation of Hunan Province,China(2022JJ40866)the Outstanding Youth Project of Education Bureau of Hunan Province,China(20B613)。
摘要Understanding the spatial distributions and corresponding variation mechanisms of key soil nutrients in fragile karst ecosystems can assist in promoting sustainable development.However,due to the implementation of ecological restoration initiatives such as land-use conversions,novel changes in the spatial characteristics of soil nutrients remain unknown.To address this gap,we explored nutrient variations and the drivers of the variation in the 0–15 cm topsoil layer using a regional-scale sampling method in a typical karst area in northwest Guangxi Zhuang Autonomous Region,Southwest China.Descriptive statistics,geostatistics,and spatial analysis were used to assess the soil nutrient variability.The results indicated that soil organic carbon(SOC),total nitrogen(TN),total phosphorus(TP),and total potassium(TK)concentrations showed moderate variations,with coefficients of variance being 0.60,0.60,0.71,and 0.72,respectively.Moreover,they demonstrated positive spatial autocorrelations,with global Moran's indices being 0.68,0.77,0.64,and 0.68,respectively.However,local Moran's index values were low,indicating large spatial variations in soil nutrients.The best-fitting semi-variogram models for SOC,TN,TP,and TK concentrations were spherical,Gaussian,exponential,and exponential,respectively.According to the classification criteria of the Second National Soil Census in China,SOC and TN concentrations were relatively sufficient,with the proportions of rich and very rich levels being up to 90.9 and 96.0%,respectively.TP concentration was in the mediumdeficient level,with the areas of medium and deficient levels accounting for 33.7 and 30.1%of the total,respectively.TK concentration was deficient,with the cumulative area of extremely deficient,very deficient,and deficient levels accounting for 87.6%of the total area.Consequently,the terrestrial ecosystems in the study area were more vulnerable to soil P and K than soil N deficiencies.Furthermore,variance partitioning analysis of the influencing factors showed that,except for the interactions,the single effect of other soil properties accounted more for soil nutrient variations than spatial and environmental variables.These results will aid in the future management of terrestrial ecosystems.
基金funded by Fundamental Research Funds of CAF(CAFYBB2023PA003)The National Key Research and Development Program of China(2023ZD0406100-03).
摘要Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments.
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
摘要Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金Supported by the Integrated Project of National Natural Science Foundation and Enterprise Innovation Development Joint Foundation(U24B6004)。
摘要Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area.
基金Supported by the National Natural Science Foundation of China,No.81770813 and No.82070866the Joint Funds for the Innovation of Science and Technology,Fujian Province,No.2023Y9453+1 种基金Shanxi Research Program of Application Foundation,No.202403021212199China Postdoctoral Science Foundation,No.2024M751910.
摘要BACKGROUND The classification of ketosis-onset diabetes is controversial,and the prevalence and clinical characteristics of diabetic retinopathy(DR)in ketosis-onset diabetes mellitus remain unclear.AIM To compare the prevalence and risk factors of DR among patients with type 1 diabetes mellitus(T1DM),ketosis-onset diabetes,and non-ketotic type 2 diabetes mellitus(T2DM).METHODS This real-world observational study enrolled 1777 patients with newly diagnosed diabetes between January 2003 and December 2012,including 211 with T1DM,673 with ketosis-onset diabetes,and 893 with non-ketotic T2DM.DR was assessed using digital nonmydriatic fundus photography,and its severity was graded based on the ETDRS classification.Clinical characteristics and risk factors of DR were compared across the three groups.RESULTS After controlling for age and sex,DR prevalence was significantly higher in ketosis-onset diabetes(9.5%)than in T1DM(5.7%,P=0.034),but not significantly different from non-ketotic T2DM(12.3%,P=0.105).Risk factors for DR in ketosis-onset diabetes and non-ketotic T2DM included increased estimated glomerular filtration rate(eGFR)and urinary albumin excretion.In contrast,elevated eGFR and 2-hour postprandial C-peptide were independent risk factors for DR in T1DM.The prevalence and risk factors for DR in ketosis-onset diabetes were similar to those in non-ketotic T2DM,but differed from T1DM.CONCLUSION These findings further support classifying ketosis-onset diabetes as a subtype of T2DM rather than idiopathic T1DM.However,the distinctive clinical features of ketosis-onset diabetes should not be ignored.
摘要为解决设施蔬菜连作障碍严重问题,明确不同轮作模式对设施蔬菜土壤微生物群落结构和功能的影响,本研究基于宏基因组测序技术,对生菜-水稻(S1)、生菜-旱稻(S2)、生菜-油麦菜(S3)3种轮作模式下的微生物群落结构和多样性、互作网络和群落功能变化等进行研究。结果表明,相较于生菜连作(CK),3种轮作模式均在不同程度上提高了土壤细菌群落α多样性,生菜-水稻轮作模式下Chao1和ACE指数增幅最大,Simpson指数降幅最大,Shannon指数在生菜-旱稻轮作模式下增幅最大;主坐标分析(PCoA)和置换多元方差分析(PERMANOVA)表明,3种轮作模式显著改变了土壤细菌群落组成,慢生根瘤菌属(Bradyrhizobium)、Gp6-AA40、PSRF01、VBCG01在3种轮作模式下均显著富集,且慢生根瘤菌属和PSRF01在生菜-水稻轮作处理下相对丰度最高;共现网络分析表明,轮作模式不仅增强了微生物网络的复杂性与稳定性,而且改变了微生物关键类群,生菜-水稻轮作模式关键类群为PSRF01、慢生根瘤菌属、鞘氨醇单胞菌属(Sphingomicrobium)、溶杆菌属(Lysobacter)等有益细菌,生菜-旱稻轮作模式关键类群为硝化螺旋菌属(Nitrospira)和鞘氨醇单胞菌属等,生菜-油麦菜轮作模式关键类群为硝化螺旋菌属和溶杆菌属等;基于KEGG(Kyoto Encyclopedia of Genes and Genomes)数据库进行功能预测,3种轮作模式下共有17个子功能基因(KEGG途径)的相对丰度显著富集,其中,膜转运、运输和分解代谢、环境适应性、糖代谢、能量代谢等12条代谢途径相关的功能基因丰度在生菜-水稻轮作模式下增幅最大。综上所述,3种轮作模式皆改变了设施蔬菜土壤微生物群落组成、多样性和功能,其中,水旱轮作模式(生菜-水稻轮作)各方面指标的效果均最佳。因此,缓解或避免设施生菜连作障碍理想的轮作模式为生菜-水稻轮作,生菜-旱稻轮作次之。
摘要This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
基金Funded by the National Key R&D Program of China(No.2023YFC3806100)the National Nature Science Foundation of China(No.52278269,52278268)+2 种基金the Tianjin Outstanding Young Scholars Science Fund Project(No.22JCJQJC00020)the Key Project of Tianjin Natural Science Foundation(No.23JCZDJC00430)the Joint Research Center of China and Foreign Countries Special Fund of Tianjin Innovation Platform(No.24PTLYHZ00240)。
摘要We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages.The experimental results show that the performance and self-healing effect of composite micro-organisms are significantly better than those of single microorganisms.For cracks with widths of 0.2-0.4 mm,the repair effect of the composite microorganisms at 28 days is 42.7%and 71.2%higher than that of pure Bacillus and pure yeast,respectively.The repairing rate of the area with the widths of the cracks of 0.2-0.4,0.4-0.6,and 0.6-0.8 mm are 100%,77.3%,and 53.4%,respectively.The area repair rates corresponding to cracking ages of 56,90,and 180 days are 73.3%,55.4%,and 30.8%,respectively.