The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflo...Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.展开更多
The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness dimin...The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) ...The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.展开更多
Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, ...Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electros...In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electrospun Poly(lactic-co-glycolic acid)/Gelatin(PLGA/Gel)membrane incorporated with Vascular Endothelial Growth Fac-tor(VEGF)and hawthorn extract.Functionally,the DP supplies native Extracellular Matrix(ECM)components and mechanical support,while PANINPs provide conductivity.The electrospun PLGA/Gel layer mimics fibrous ECM.It incorporates bioactives,with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhanc-ing anticoagulant activity,as well as increasing surface hydrophilicity.The tissue adhesive ensures the interfacial integrity between the two layers.Decellularization efficiency was confirmed histologically using 4',6-diamidino-2-phenylindole(DAPI)and Hematoxylin-Eosin(H&E)staining.The DP exhibited a DNA content of 115.9±47.8 ng/mg DNA,compared to 982.88±395.42 ng/mg in Native Pericardium(NP).The PANINPs had an average par-ticle size of 104.94±13.7 nm.The conductivity of PANINPs-coated decellularized pericardium was measured to be 9.093±8.6×10-4 S/cm using the four-point probe method.PLGA/Gel membranes containing hawthorn extract(1%,5%,10%,and 15%w/v)and VEGF(0.1μg/mL,0.5μg/mL,and 1μg/mL)were fabricated by electrospinning,result-ing in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20μm.The anticoagulant efficiency of the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay(aPTT).Mechanical testing revealed a tensile strength of 22.70±6.33 MPa,an elongation of 53.58±10.63%,and Young's modulus of 0.67±0.10 MPa.The scaffold also exhibited over 91%cell viability and excellent cardiomyo-cyte adhesion.The hemolysis ratio was determined to be 0.421±0.191%,which confirms its blood compatibility.Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.展开更多
AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on...AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.展开更多
AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. ...AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.展开更多
Forest ecosystems worldwide can be affected by extreme climatic events.Trees respond to these occurrences in multidimensional ways,involving various mechanisms,to deal with the effects and restore the forests to their...Forest ecosystems worldwide can be affected by extreme climatic events.Trees respond to these occurrences in multidimensional ways,involving various mechanisms,to deal with the effects and restore the forests to their optimal state.Such abilities are known as resilience.Tree ring analysis can be used to evaluate drought resilience.Analysis of dendrophenotypes,together with genetic studies,has become an essential tool for identifying drought resilient genotypes.This study aimed to determine the dendrogenomic resilience mechanisms in the fragmented,isolated,rare endemic Mexican species Picea martinezii and P.mexicana by analysis of annual rings and the associations with SNP markers identified by genotyping by sequencing(GBS).Increment cores and needles for GBS for resilience analysis were collected from P.martinezii trees in three populations,and from P.mexicana trees in two populations.The results show that fundamental dendrogenomic mechanisms were associated with drought resilience in P.martinezii and P.mexicana.PC1 in PCA for five outlier SNPs was linked to annual tracheid width variations in P.martinezii caused by severe drought events in 1962,1989,1998 and 2011.These five outlier SNPs were located in genes coding the proteins reticulon-like protein B22,pollen-specific leucine-rich repeat extension,ornithine decarboxylase like,LisH/CRA/RING-U-box domains-containing protein and proline transporter 2-like isoform X1,which are important in the dry stress tolerance metabolism involved in the resilience response in plants.The discovery of genetic markers associated with drought resilience highlights the importance of preserving genetic diversity.展开更多
Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tr...Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tree carbon stocks in soils with different mycorrhizal types are affected by soil fertility and forest structure.Tree demographic data were used from a 21.12-ha study area collected over a ten-year period(2009-2019),covering 43species of woody plants and more than 50,000 individuals.Relationships between tree carbon stock,soil fertility and forest structure(stand density,diameter variation,species diversity and spatial distribution)were examined,as well as whether these relationships differed between arbuscular mycorrhiza and ectomycorrhizal mycorrhiza groups in a typical temperate conifer and broad-leaved mixed forest.We found that total tree carbon stock was positively impacted by variations in stand density and tree diameter but negatively influenced by soil fertility,tree species diversity and uniform angle index.Soil fertility promoted carbon stock of trees associated with arbuscular mycorrhiza(AM)but inhibited the carbon stock of trees with ectomycorrhizal mycorrhiza fungi(EcM).Carbon stock of AM trees was mainly influenced by soil fertility,while carbon stock of EcM trees was influenced by stand density.Our findings show that mycorrhizae types mediate the impact of stand structure and soil fertility on tree carbon stocks and provides new evidence on how forest tree carbon stocks may be enhanced based on the types of mycorrhizal associations.Tree species with different mycorrhizal types can be managed in different ways.展开更多
The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by ...The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.展开更多
In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-sp...In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.展开更多
Brazil maintains a leading position in agricultural exports and stands as the world's foremost producer and user of bioinputs in agriculture.These bioinputs generate annual savings of billions of dollars that woul...Brazil maintains a leading position in agricultural exports and stands as the world's foremost producer and user of bioinputs in agriculture.These bioinputs generate annual savings of billions of dollars that would otherwise be allocated to chemical fertilizers and pesticides.The nation's regulatory framework enables bioinput agriculture and serves as a model for countries transitioning toward regenerative agriculture.Brazilian legislation categorizes bioinputs into:1)biofertilizers(extracts);2)biostimulants(plant growth-promoting and biocontrol agents);and 3)inoculants(active ingredient comprises one or more living microorganisms).The inoculation of soybeans with Bradyrhizobium strains provides approximately 90%of the nitrogen accumulated by this crop.Brazil has registered over six hundred inoculants,with at least 60%specifically designated for soybean cultivation.The annual sales of inoculants in Brazil reach approximately 120 million doses.Although beans(Phaseolus vulgaris and Vigna unguiculata)represent an essential food crop in Brazil's staple diet and benefit from inoculation,inoculant supply remains insufficient.Regarding biocontrol,soy,corn,sugarcane,and coffee rank among the most protected crops,employing biocontrol agents against bacteria,fungi,nematodes,and insects.Bacillus,Pseudomonas,Streptomyces,Rhizobium,Azotobacter,and Paenibacillus strains were predominantly cited in the 5,000+bioproduct patents filed between 2022 and 2024.Among fungal genera,Trichoderma,and Penicillium received the most citations.EMBRAPA's biobanks maintain over 10,000 strains of bacteria,fungi,and viruses for biocontrol,and 14,000 strains of nutrient-fixing and plant-growth promoters.Production challenges include quality control,particularly as on-farm production of inoculants becomes prevalent on larger farms,alongside product availability and supply limitations.Brazilian farmers maintain global competitiveness partly through reduced chemical fertilizer and pesticide costs enabled by bioinput usage.As components of regenerative agriculture,bioinputs enhance soil quality,decrease carbon footprints,and support Sustainable Development Goals.Brazil's leadership in microbial bioinput utilization stems from its extensive agricultural sector,rich microbial biodiversity,and progressive regulatory framework.展开更多
Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays...Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.展开更多
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
文摘Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62276109The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Research Group Project number(ORF-2025-585).
文摘The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金supported by the National Key Research and Development Program of Chinathe National Natural Science Foundation of China (Grant Nos.2024YFA1408000,12474097,and2023YFA1406001)+2 种基金the Guangdong Provincial Quantum Science Strategic Initiative (Grant No.GDZX2201001)the Center for Computational Science and Engineering at Southern University of Science and Technology,the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen(for J.L.Z.and Y.L.)the Chinese funding sources applied via HPSTAR。
文摘The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.
文摘Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
文摘In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electrospun Poly(lactic-co-glycolic acid)/Gelatin(PLGA/Gel)membrane incorporated with Vascular Endothelial Growth Fac-tor(VEGF)and hawthorn extract.Functionally,the DP supplies native Extracellular Matrix(ECM)components and mechanical support,while PANINPs provide conductivity.The electrospun PLGA/Gel layer mimics fibrous ECM.It incorporates bioactives,with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhanc-ing anticoagulant activity,as well as increasing surface hydrophilicity.The tissue adhesive ensures the interfacial integrity between the two layers.Decellularization efficiency was confirmed histologically using 4',6-diamidino-2-phenylindole(DAPI)and Hematoxylin-Eosin(H&E)staining.The DP exhibited a DNA content of 115.9±47.8 ng/mg DNA,compared to 982.88±395.42 ng/mg in Native Pericardium(NP).The PANINPs had an average par-ticle size of 104.94±13.7 nm.The conductivity of PANINPs-coated decellularized pericardium was measured to be 9.093±8.6×10-4 S/cm using the four-point probe method.PLGA/Gel membranes containing hawthorn extract(1%,5%,10%,and 15%w/v)and VEGF(0.1μg/mL,0.5μg/mL,and 1μg/mL)were fabricated by electrospinning,result-ing in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20μm.The anticoagulant efficiency of the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay(aPTT).Mechanical testing revealed a tensile strength of 22.70±6.33 MPa,an elongation of 53.58±10.63%,and Young's modulus of 0.67±0.10 MPa.The scaffold also exhibited over 91%cell viability and excellent cardiomyo-cyte adhesion.The hemolysis ratio was determined to be 0.421±0.191%,which confirms its blood compatibility.Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.
文摘AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.
基金Supported by the Grants From Ligue Nationale Contre le Cancer,Comités Départementaux de la Manche,de l'Orne et du Calvados and from Université de Metz
文摘AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.
基金funding from the Mixed Fund of the National Council of Humanities,Sciences,and Technologies of Mexico and the National Forestry Commission(CONACYT-CONAFOR-2017-4-292615),awarded to Christian WehenkelSECIHTI provided a graduate scholarship to Carlos Alberto Segura Sanchez(776540).
文摘Forest ecosystems worldwide can be affected by extreme climatic events.Trees respond to these occurrences in multidimensional ways,involving various mechanisms,to deal with the effects and restore the forests to their optimal state.Such abilities are known as resilience.Tree ring analysis can be used to evaluate drought resilience.Analysis of dendrophenotypes,together with genetic studies,has become an essential tool for identifying drought resilient genotypes.This study aimed to determine the dendrogenomic resilience mechanisms in the fragmented,isolated,rare endemic Mexican species Picea martinezii and P.mexicana by analysis of annual rings and the associations with SNP markers identified by genotyping by sequencing(GBS).Increment cores and needles for GBS for resilience analysis were collected from P.martinezii trees in three populations,and from P.mexicana trees in two populations.The results show that fundamental dendrogenomic mechanisms were associated with drought resilience in P.martinezii and P.mexicana.PC1 in PCA for five outlier SNPs was linked to annual tracheid width variations in P.martinezii caused by severe drought events in 1962,1989,1998 and 2011.These five outlier SNPs were located in genes coding the proteins reticulon-like protein B22,pollen-specific leucine-rich repeat extension,ornithine decarboxylase like,LisH/CRA/RING-U-box domains-containing protein and proline transporter 2-like isoform X1,which are important in the dry stress tolerance metabolism involved in the resilience response in plants.The discovery of genetic markers associated with drought resilience highlights the importance of preserving genetic diversity.
基金supported by the Science and Technology Project of the Department of Transportation of Heilongjiang Province(HJK2023B024-3)the National Key R&D Program of China(2023YFF1304001-01)。
文摘Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tree carbon stocks in soils with different mycorrhizal types are affected by soil fertility and forest structure.Tree demographic data were used from a 21.12-ha study area collected over a ten-year period(2009-2019),covering 43species of woody plants and more than 50,000 individuals.Relationships between tree carbon stock,soil fertility and forest structure(stand density,diameter variation,species diversity and spatial distribution)were examined,as well as whether these relationships differed between arbuscular mycorrhiza and ectomycorrhizal mycorrhiza groups in a typical temperate conifer and broad-leaved mixed forest.We found that total tree carbon stock was positively impacted by variations in stand density and tree diameter but negatively influenced by soil fertility,tree species diversity and uniform angle index.Soil fertility promoted carbon stock of trees associated with arbuscular mycorrhiza(AM)but inhibited the carbon stock of trees with ectomycorrhizal mycorrhiza fungi(EcM).Carbon stock of AM trees was mainly influenced by soil fertility,while carbon stock of EcM trees was influenced by stand density.Our findings show that mycorrhizae types mediate the impact of stand structure and soil fertility on tree carbon stocks and provides new evidence on how forest tree carbon stocks may be enhanced based on the types of mycorrhizal associations.Tree species with different mycorrhizal types can be managed in different ways.
文摘The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.
基金the National Science Foundation of China (Grant No. NSFC: 92162213)the Geology Department Faculty of Science of Al-Azhar University (Assiut Branch)+2 种基金the China Scholarship CouncilChang'an UniversityIstanbul Technical University's Scientific Research Project (BAP Project ID: 45396, code: FHD-2024-45396)
文摘In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.
基金funded in part by the Postgraduate Program of the Paulo de Góes Institute of Microbiology,Federal University of Rio de Janeiro(UFRJ),through the coordination of Higher Education Personnel Improvement(CAPES)(001),Brazilthe National Council for Scientific and Technological Development of Brazil(MCTI-CNPq)(309461/2019-7)+3 种基金the Rio de Janeiro State Research Support Foundation(FAPERJ),E26/200428/2023,Brazilthe support of Embrapa Agrobiology,the State Secretariat for Economic Development,Industry,Trade,and Services(SEDEICS),Brazilthe National Association for the Promotion and Innovation of the Biological Industry(ANPII-BIO)the CropLife biological products,and the Brazilian National Institute of Industrial Property(INPI)。
文摘Brazil maintains a leading position in agricultural exports and stands as the world's foremost producer and user of bioinputs in agriculture.These bioinputs generate annual savings of billions of dollars that would otherwise be allocated to chemical fertilizers and pesticides.The nation's regulatory framework enables bioinput agriculture and serves as a model for countries transitioning toward regenerative agriculture.Brazilian legislation categorizes bioinputs into:1)biofertilizers(extracts);2)biostimulants(plant growth-promoting and biocontrol agents);and 3)inoculants(active ingredient comprises one or more living microorganisms).The inoculation of soybeans with Bradyrhizobium strains provides approximately 90%of the nitrogen accumulated by this crop.Brazil has registered over six hundred inoculants,with at least 60%specifically designated for soybean cultivation.The annual sales of inoculants in Brazil reach approximately 120 million doses.Although beans(Phaseolus vulgaris and Vigna unguiculata)represent an essential food crop in Brazil's staple diet and benefit from inoculation,inoculant supply remains insufficient.Regarding biocontrol,soy,corn,sugarcane,and coffee rank among the most protected crops,employing biocontrol agents against bacteria,fungi,nematodes,and insects.Bacillus,Pseudomonas,Streptomyces,Rhizobium,Azotobacter,and Paenibacillus strains were predominantly cited in the 5,000+bioproduct patents filed between 2022 and 2024.Among fungal genera,Trichoderma,and Penicillium received the most citations.EMBRAPA's biobanks maintain over 10,000 strains of bacteria,fungi,and viruses for biocontrol,and 14,000 strains of nutrient-fixing and plant-growth promoters.Production challenges include quality control,particularly as on-farm production of inoculants becomes prevalent on larger farms,alongside product availability and supply limitations.Brazilian farmers maintain global competitiveness partly through reduced chemical fertilizer and pesticide costs enabled by bioinput usage.As components of regenerative agriculture,bioinputs enhance soil quality,decrease carbon footprints,and support Sustainable Development Goals.Brazil's leadership in microbial bioinput utilization stems from its extensive agricultural sector,rich microbial biodiversity,and progressive regulatory framework.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2022YFA1404204 and 2022YFA1403700)the National Natural Science Foundation of China (Grant Nos. 12274086, 11534001 and 11925402)+5 种基金funding from the National Science Foundation of China (Grant Nos. 12274046, 11874094, 12147102, and 12347101)Chongqing Natural Science Foundation (Grant No. CSTB2022NSCQ-JQX0018)the Fundamental Research Funds for the Central Universities (Grant No. 2021CDJZYJH-003)Xiaomi Foundation/Xiaomi Young Talents Programthe supports of the start-up funding of Westlake Universitysupport from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants。
文摘Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.
