Stroke is a major cause of death and disability worldwide.It is characterized by a highly interconnected and multiphasic neuropathological cascade of events,in which an intense and protracted inflammatory response pla...Stroke is a major cause of death and disability worldwide.It is characterized by a highly interconnected and multiphasic neuropathological cascade of events,in which an intense and protracted inflammatory response plays a crucial role in worsening brain injury.Neuroinflammation,a key player in the pathophysiology of stroke,has a dual role.In the acute phase of stroke,neuroinflammation exacerbates brain injury,contributing to neuronal damage and blood–brain barrier disruption.This aspect of neuroinflammation is associated with poor neurological outcomes.Conversely,in the recovery phase following stroke,neuroinflammation facilitates brain repair processes,including neurogenesis,angiogenesis,and synaptic plasticity.The transition of neuroinflammation from a harmful to a reparative role is not well understood.Therefore,this review seeks to explore the mechanisms underlying this transition,with the goal of informing the development of therapeutic interventions that are both time-and context-specific.This review aims to elucidate the complex and dual role of neuroinflammation in stroke,highlighting the main actors,biomarkers of the disease,and potential therapeutic approaches.展开更多
Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of elec...Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.展开更多
BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidec...BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.展开更多
This paper explores the algebraic essence of universal logic functions(ULFs)from an algebraic perspective.Under the framework of semi-tensor product of matrices,the“sequential nature”of ULFs is revealed.Utilizing th...This paper explores the algebraic essence of universal logic functions(ULFs)from an algebraic perspective.Under the framework of semi-tensor product of matrices,the“sequential nature”of ULFs is revealed.Utilizing the nature,a technique called universal transformation method is proposed,by which any ULF can be transformed into an equivalent expression with desired features that facilitate achieving specific objectives,such as modeling,analyzing and synthesizing universal logical systems.Furthermore,several useful logical operators are constructed in a mixed-dimensional situation,including power-raising operator,power-descending operator,erasure operator,and appending operator.Finally,these results are applied to model and analyze finite state machines and their networks,which demonstrate the practical value of the method and operators.展开更多
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate...Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.展开更多
The capacity of the central nervous system for structural plasticity and regeneration is commonly believed to show a decreasing progression from“small and simple”brains to the larger,more complex brains of mammals.H...The capacity of the central nervous system for structural plasticity and regeneration is commonly believed to show a decreasing progression from“small and simple”brains to the larger,more complex brains of mammals.However,recent findings revealed that some forms of neural plasticity can show a reverse trend.Although plasticity is a well-preserved,transversal feature across the animal world,a variety of cell populations and mechanisms seem to have evolved to enable structural modifications to take place in widely different brains,likely as adaptations to selective pressures.Increasing evidence now indicates that a trade-off has occurred between regenerative(mostly stem cell–driven)plasticity and developmental(mostly juvenile)remodeling,with the latter primarily aimed not at brain repair but rather at“sculpting”the neural circuits based on experience.In particular,an evolutionary trade-off has occurred between neurogenic processes intended to support the possibility of recruiting new neurons throughout life and the different ways of obtaining new neurons,and between the different brain locations in which plasticity occurs.This review first briefly surveys the different types of plasticity and the complexity of their possible outcomes and then focuses on recent findings showing that the mammalian brain has a stem cell–independent integration of new neurons into pre-existing(mature)neural circuits.This process is still largely unknown but involves neuronal cells that have been blocked in arrested maturation since their embryonic origin(also termed“immature”or“dormant”neurons).These cells can then restart maturation throughout the animal's lifespan to become functional neurons in brain regions,such as the cerebral cortex and amygdala,that are relevant to high-order cognition and emotions.Unlike stem cell–driven postnatal/adult neurogenesis,which significantly decreases from small-brained,short-living species to large-brained ones,immature neurons are particularly abundant in large-brained,long-living mammals,including humans.The immature neural cell populations hosted in these complex brains are an interesting example of an“enlarged road”in the phylogenetic trend of plastic potential decreases commonly observed in the animal world.The topic of dormant neurons that covary with brain size and gyrencephaly represents a prospective turning point in the field of neuroplasticity,with important translational outcomes.These cells can represent a reservoir of undifferentiated neurons,potentially granting plasticity within the high-order circuits subserving the most sophisticated cognitive skills that are important in the growing brains of young,healthy individuals and are frequently affected by debilitating neurodevelopmental and degenerative disorders.展开更多
Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study...Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.展开更多
The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-effic...The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.展开更多
The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are havi...The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are having a significant impact,particularly on global poverty governance.As a platform for dialogue,exchange and technical cooperation,the 2025 International Seminar on Global Poverty Reduction Partnerships was held in Beijing on 10 December 2025.展开更多
Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screenin...Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.展开更多
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the ...This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Tr...Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Traditional approaches have focused on symptom management,but breakthroughs in bioinformatics and high-thro ughput drug screening are offering new pathways to potential therapies.This review highlights our recent effo rts to identify novel drug candidates for Alzheimer's disease by leve raging computational methods and la rge-scale biological datasets.Our work introduces two key innovations in Alzheimer's disease research:addressing sex-specific diffe rences and leve raging drug repurposing for accelerated treatment discove ry.By combining sex-stratified preclinical data with machine learning and in vivo validation,we improve translational relevance and support precision medicine.Using the TgF344-AD rat model,which mimics human Alzheimer's disease spatial memory deficits and pathology,we explored the efficacy of various US Food and Drug Administrationapproved and investigational drugs.These included ibudilast,timapiprant,RG2833,diazoxide/dibenzoylmethane(combined),and BT-11,which targeted key Alzheimer's disease-related molecular pathways such as amyloid-beta plaques,Ta u tangles,and neuroinflammation.These drugs,at various stages of development,offer hope for not only managing symptoms but also addressing the underlying mechanisms of Alzheimer's disease.This review underscores the need for a multifaceted approach to Alzheimer's disease treatment,combining symptom relief with disease modification.展开更多
Based on the Smit-Suhl formula,we propose a universal approach for solving the magnon-magnon coupling problem in bilayer coupled systems(e.g.,antiferromagnets).This method requires only the energy expression,enabling ...Based on the Smit-Suhl formula,we propose a universal approach for solving the magnon-magnon coupling problem in bilayer coupled systems(e.g.,antiferromagnets).This method requires only the energy expression,enabling the automatic derivation of analytical expressions for the eigenmatrix elements via symbolic computation,eliminating the need for tedious manual calculations.Using this approach,we investigate the impact of magnetic hysteresis on magnon-magnon coupling in a system with interlayer Dzyaloshinskii-Moriya interaction(DMI).The magnetic hysteresis leads to an asymmetric magnetic field dependence of the resonance frequency and alters the number of degeneracy points between the pure optical and acoustic modes.Moreover,it can result in the coupling strength at the gap of the f–H phase diagram being nearly vanishing,contrary to the conventionally expected maximum.These results deepen the understanding of the effect of interlayer DMI on magnon–magnon coupling and the proposed universal method significantly streamlines the solving process of magnon–magnon coupling problems.展开更多
A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi...The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.展开更多
Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification,yet where net primary productivity(NPP)degradation is likely to persist and what drives it remain unclear in the Tiansh...Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification,yet where net primary productivity(NPP)degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China.We integrated multi-source remote sensing with the Carnegie–Ames–Stanford Approach(CASA)model to estimate NPP during 2000–2020,assessed trend persistence using the Hurst exponent,and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting(XGBoost)and SHapley Additive exPlanations(SHAP).Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020,while regional mean NPP rose from 138.97 to 160.69 g C/(m^(2)·a).Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall.Although NPP increased across 64.11%of the region during 2000–2020,persistence analysis suggested that 53.93%of the Tianshan Mountains was prone to continued NPP decline,including 36.41%with significant projected decline and 17.52%with weak projected decline;these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains.In contrast,potential improvement was limited(strong persistent improvement:4.97%;strong anti-persistent improvement:0.36%).Driver attribution indicated that land use dominated NPP variability(mean absolute SHAP value=29.54%),followed by precipitation(16.03%)and temperature(11.05%).SHAP dependence analyses showed that precipitation effects stabilized at 300.00–400.00 mm,and temperature exhibited an inverted U-shaped response with a peak near 0.00°C.These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion,highlighting the need for threshold-informed,spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.展开更多
Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exh...Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases.Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders.To investigate this heterogeneity,we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions.This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein(GfaABC1D)promoter.The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions.The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images.Furthermore,the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation.This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases.The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions.展开更多
基金supported by European Union-NextGeneration EU under the Italian University and Research(MUR)National Innovation Ecosystem grant ECS00000041-VITALITY-CUP E13C22001060006(to MdA)。
文摘Stroke is a major cause of death and disability worldwide.It is characterized by a highly interconnected and multiphasic neuropathological cascade of events,in which an intense and protracted inflammatory response plays a crucial role in worsening brain injury.Neuroinflammation,a key player in the pathophysiology of stroke,has a dual role.In the acute phase of stroke,neuroinflammation exacerbates brain injury,contributing to neuronal damage and blood–brain barrier disruption.This aspect of neuroinflammation is associated with poor neurological outcomes.Conversely,in the recovery phase following stroke,neuroinflammation facilitates brain repair processes,including neurogenesis,angiogenesis,and synaptic plasticity.The transition of neuroinflammation from a harmful to a reparative role is not well understood.Therefore,this review seeks to explore the mechanisms underlying this transition,with the goal of informing the development of therapeutic interventions that are both time-and context-specific.This review aims to elucidate the complex and dual role of neuroinflammation in stroke,highlighting the main actors,biomarkers of the disease,and potential therapeutic approaches.
基金supported by the National Natural Science Foundation of China(No.U23A20651)the Central Government Guides Local Science and Technology Development Foundation(No.2023ZYDF022)+1 种基金the Sichuan Science and Technology Program(2024ZDZX0031)the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(No.SKLMRDPC23KF19).
文摘Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.
文摘BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.
基金supported in part by the National Natural Science Foundation of China under Grants 62073124 and U1804150.
文摘This paper explores the algebraic essence of universal logic functions(ULFs)from an algebraic perspective.Under the framework of semi-tensor product of matrices,the“sequential nature”of ULFs is revealed.Utilizing the nature,a technique called universal transformation method is proposed,by which any ULF can be transformed into an equivalent expression with desired features that facilitate achieving specific objectives,such as modeling,analyzing and synthesizing universal logical systems.Furthermore,several useful logical operators are constructed in a mixed-dimensional situation,including power-raising operator,power-descending operator,erasure operator,and appending operator.Finally,these results are applied to model and analyze finite state machines and their networks,which demonstrate the practical value of the method and operators.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.
基金supported by Progetto Trapezio,Compagnia di San Paolo(67935-2021.2174),to LBFondazione CRT(Cassa di Risparmio di Torino,RF=2022.0618),to LBPRIN2022(grant 2022LB4X3N),to LB。
文摘The capacity of the central nervous system for structural plasticity and regeneration is commonly believed to show a decreasing progression from“small and simple”brains to the larger,more complex brains of mammals.However,recent findings revealed that some forms of neural plasticity can show a reverse trend.Although plasticity is a well-preserved,transversal feature across the animal world,a variety of cell populations and mechanisms seem to have evolved to enable structural modifications to take place in widely different brains,likely as adaptations to selective pressures.Increasing evidence now indicates that a trade-off has occurred between regenerative(mostly stem cell–driven)plasticity and developmental(mostly juvenile)remodeling,with the latter primarily aimed not at brain repair but rather at“sculpting”the neural circuits based on experience.In particular,an evolutionary trade-off has occurred between neurogenic processes intended to support the possibility of recruiting new neurons throughout life and the different ways of obtaining new neurons,and between the different brain locations in which plasticity occurs.This review first briefly surveys the different types of plasticity and the complexity of their possible outcomes and then focuses on recent findings showing that the mammalian brain has a stem cell–independent integration of new neurons into pre-existing(mature)neural circuits.This process is still largely unknown but involves neuronal cells that have been blocked in arrested maturation since their embryonic origin(also termed“immature”or“dormant”neurons).These cells can then restart maturation throughout the animal's lifespan to become functional neurons in brain regions,such as the cerebral cortex and amygdala,that are relevant to high-order cognition and emotions.Unlike stem cell–driven postnatal/adult neurogenesis,which significantly decreases from small-brained,short-living species to large-brained ones,immature neurons are particularly abundant in large-brained,long-living mammals,including humans.The immature neural cell populations hosted in these complex brains are an interesting example of an“enlarged road”in the phylogenetic trend of plastic potential decreases commonly observed in the animal world.The topic of dormant neurons that covary with brain size and gyrencephaly represents a prospective turning point in the field of neuroplasticity,with important translational outcomes.These cells can represent a reservoir of undifferentiated neurons,potentially granting plasticity within the high-order circuits subserving the most sophisticated cognitive skills that are important in the growing brains of young,healthy individuals and are frequently affected by debilitating neurodevelopmental and degenerative disorders.
基金supported by the Iran National Science Foundation(INSF)the University of Birjand under grant number 4034771.
文摘Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
基金supported by the National Key Research and Development Program of China(2022YFC2402400)the National Natural Science Foundation of China(82027803,62275062)+7 种基金the Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology(2020B121201010)the Shenzhen Science and Technology Innovation Committee under Grant(JCYJ20220818101417039)the Shenzhen Key Laboratory for Molecular lmaging(ZDSY20130401165820357)the Shenzhen Medical Research Fund(D2404002)the Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments(2023-SGTTXM-002 and 2024-SGTTXM-005)the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)(YDZX2023115)the Taishan Scholar Special Funding Project of Shandong Provinceand the Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai(ZL202402).
文摘The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.
文摘The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are having a significant impact,particularly on global poverty governance.As a platform for dialogue,exchange and technical cooperation,the 2025 International Seminar on Global Poverty Reduction Partnerships was held in Beijing on 10 December 2025.
文摘Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金supported by the Science Center Program of National Natural Science Foundation of China under Grant 62188101the National Natural Science Foundation of China under Grant 62573265.
文摘This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
基金National Institutes of Health,No.R01AG057555(to PI,L.Xie,co-l,MEFP,PAS,PR)。
文摘Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Traditional approaches have focused on symptom management,but breakthroughs in bioinformatics and high-thro ughput drug screening are offering new pathways to potential therapies.This review highlights our recent effo rts to identify novel drug candidates for Alzheimer's disease by leve raging computational methods and la rge-scale biological datasets.Our work introduces two key innovations in Alzheimer's disease research:addressing sex-specific diffe rences and leve raging drug repurposing for accelerated treatment discove ry.By combining sex-stratified preclinical data with machine learning and in vivo validation,we improve translational relevance and support precision medicine.Using the TgF344-AD rat model,which mimics human Alzheimer's disease spatial memory deficits and pathology,we explored the efficacy of various US Food and Drug Administrationapproved and investigational drugs.These included ibudilast,timapiprant,RG2833,diazoxide/dibenzoylmethane(combined),and BT-11,which targeted key Alzheimer's disease-related molecular pathways such as amyloid-beta plaques,Ta u tangles,and neuroinflammation.These drugs,at various stages of development,offer hope for not only managing symptoms but also addressing the underlying mechanisms of Alzheimer's disease.This review underscores the need for a multifaceted approach to Alzheimer's disease treatment,combining symptom relief with disease modification.
基金supported by the National Key Research and Development Program of China (MOST)(Grant No.2022YFA1402800)the Chinese Academy of Sciences (CAS) Presidents International Fellowship Initiative (PIFI)(Grant No.2025PG0006)+3 种基金the National Natural Science Foundation of China (NSFC)(Grant Nos.51831012,12274437,and 52161160334)the CAS Project for Young Scientists in Basic Research (Grant No.YSBR-084)the CAS Youth Interdisciplinary Teamthe China Postdoctoral Science Foundation (Grant No.2025M773402)。
文摘Based on the Smit-Suhl formula,we propose a universal approach for solving the magnon-magnon coupling problem in bilayer coupled systems(e.g.,antiferromagnets).This method requires only the energy expression,enabling the automatic derivation of analytical expressions for the eigenmatrix elements via symbolic computation,eliminating the need for tedious manual calculations.Using this approach,we investigate the impact of magnetic hysteresis on magnon-magnon coupling in a system with interlayer Dzyaloshinskii-Moriya interaction(DMI).The magnetic hysteresis leads to an asymmetric magnetic field dependence of the resonance frequency and alters the number of degeneracy points between the pure optical and acoustic modes.Moreover,it can result in the coupling strength at the gap of the f–H phase diagram being nearly vanishing,contrary to the conventionally expected maximum.These results deepen the understanding of the effect of interlayer DMI on magnon–magnon coupling and the proposed universal method significantly streamlines the solving process of magnon–magnon coupling problems.
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023E01006,2024TSYCCX0004).
文摘Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification,yet where net primary productivity(NPP)degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China.We integrated multi-source remote sensing with the Carnegie–Ames–Stanford Approach(CASA)model to estimate NPP during 2000–2020,assessed trend persistence using the Hurst exponent,and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting(XGBoost)and SHapley Additive exPlanations(SHAP).Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020,while regional mean NPP rose from 138.97 to 160.69 g C/(m^(2)·a).Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall.Although NPP increased across 64.11%of the region during 2000–2020,persistence analysis suggested that 53.93%of the Tianshan Mountains was prone to continued NPP decline,including 36.41%with significant projected decline and 17.52%with weak projected decline;these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains.In contrast,potential improvement was limited(strong persistent improvement:4.97%;strong anti-persistent improvement:0.36%).Driver attribution indicated that land use dominated NPP variability(mean absolute SHAP value=29.54%),followed by precipitation(16.03%)and temperature(11.05%).SHAP dependence analyses showed that precipitation effects stabilized at 300.00–400.00 mm,and temperature exhibited an inverted U-shaped response with a peak near 0.00°C.These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion,highlighting the need for threshold-informed,spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.
基金National Natural Science Foundation of China,No.32271148(to JW)the National Key Research and the Development Program of China,No.2023M740625(to ML)+1 种基金the Natural Science Foundation of Guangdong Province,Nos.2021B1515120050(to HW)and 2023A1515110782(to ML)and Key R&D Program of Ningxia Hui Autonomous Region,No.2024BEG02027(to JW).
文摘Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases.Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders.To investigate this heterogeneity,we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions.This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein(GfaABC1D)promoter.The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions.The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images.Furthermore,the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation.This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases.The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions.
