This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distr...This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distribution function that increases with an increase in energy at zero electron energy.The inverse EEDF plays a central role in the problem of negative conductivity.Based on the previously obtained criterion for the formation of an inverse EEDF in a spatially inhomogeneous plasma,a heuristic method is proposed that allows one to avoid resource-intensive calculations for spatially two-dimensional(2D)kinetic modeling on a large array of different glow discharges.It is shown that the conditions for EEDF inversion can be realized in two-chamber discharge structures due to violating the known Boltzmann distribution for electron density.The theoretical conclusions are validated by numerical modeling of lowpressure two-chamber inductively-coupled plasma(ICP)discharges in the COMSOL Multiphysics environment.As a result,areas of conditions with inverse EEDF were found for subsequent detailed kinetic analysis and experimental studies.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synth...A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.展开更多
This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared ...This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we...This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.展开更多
Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the bi...Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the biosynthetic pathway of saikosaponins,we identified two cytochrome P450 monooxygenases,CYP716A41 and CYP716Y4,in Bupleurum chinense.These enzymes catalyze the C-28 oxidation and C-16 hydroxylation of oleanane-type triterpene skeletons,respectively.The catalytic efficiency of CYP716A41 from a southern B.chinense variety was significantly higher than that from a northern variety.Molecular docking and mutagenesis experiments revealed that amino acid residues at sites 9 and 35 may contribute to this difference in catalytic efficiency.Additionally,under cold stress,the expression levels of both CYP450 genes and the saikosaponin contents in the leaves of southern varieties were significantly higher compared to those in northern varieties.The variation in the catalytic efficiency of CYP716A41 and the differential expression of the two CYP450 genes under cold stress during winter are associated with the differences in saikosaponin biosynthesis in the leaves of southern and northern B.chinense varieties.This is consistent with the distinct medicinal usage practices observed between southern and northern China.展开更多
Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in s...Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.展开更多
Nickel-catalyzed borylation of aryl nonaflates with B2pin2 could be realized,which proceeded effectively by means of C—O bond functionalization to afford a wide variety of valuable arylboronates in moderate to excell...Nickel-catalyzed borylation of aryl nonaflates with B2pin2 could be realized,which proceeded effectively by means of C—O bond functionalization to afford a wide variety of valuable arylboronates in moderate to excellent yields with good functionality compatibility.In addition,the gram-scale synthesis and the application of the approach in the late-stage elaboration of aryl nonaflate derived from pterostilbene could also be achieved.展开更多
Let K_(j)/Q,1≤j≤ν,ν≥2 be quadratic fields with pairwise coprime discriminants Dj,and let τ_(kj)^(K_(j))(n)be the divisor function associated to Dedekind zeta function SK_(j)(s).In this paper,we consider a multid...Let K_(j)/Q,1≤j≤ν,ν≥2 be quadratic fields with pairwise coprime discriminants Dj,and let τ_(kj)^(K_(j))(n)be the divisor function associated to Dedekind zeta function SK_(j)(s).In this paper,we consider a multidimensional general divisor problem related to the τ_(kj)^(K_(j))(n)involving several number fields over square integers,by establishing the corresponding asymptotic formula.As an application,we also obtain the asymptotic formula of variance of these coefi icients.展开更多
In recent years,researchers have extensively investigated the Hankel determinant,which consists of coefficients appearing in a holomorphic function’s Taylor-Maclaurin series.Hankel matrices are widely used in Markov ...In recent years,researchers have extensively investigated the Hankel determinant,which consists of coefficients appearing in a holomorphic function’s Taylor-Maclaurin series.Hankel matrices are widely used in Markov processes,non-stationary signals,and other mathematical disciplines.The aim of the current research article is to first improve the bounds of coefficient-related problems by employing the well-known Carathéodory function.The problems that we are going to improve were obtained by Tang et al.The sharp estimates of the most difficult problem of geometric function theory known as the third-order Hankel determinant are also contributed here.Zalcman and Fekete-Szegöinequalities are also studied here for the defined family of holomorphic functions.展开更多
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fati...This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.展开更多
Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to s...Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which req...Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.展开更多
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ...Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios.展开更多
In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the...In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the critical elementary reaction of molybdenum catalysis.However,the relevant density functional theory(DFT)studies are relatively scarce,especially regarding the rational selection of functionals.In this work,14 functionals were employed to investigate the Mo-catalyzed carbonyl oxidative addition step.A benchmark study was carried out to evaluate their performance in structure optimization and energy calculation.Analyses of mean absolute error(MAE)and mean squared error(MSE)indicated that the B3LYP-D3(BJ),TPSSh,and ωB97X-D functionals exhibited superior performance in structure optimization.Using the DLPNO-CCSD(T)functional as the reference,the M06,M06-L,and MN15-L functionals exhibited good performance for energy calculation based on the structures optimized using the B3LYP-D3(BJ)functional.In particular,MN15-L provided the best performance with the smallest MAE and MSE.展开更多
Aspergillus species are ubiquitous fungi that produce mycotoxins(secondary metabolites)known as sterigmatocystin and aflatoxins in many different kinds of foods,which leads to serious contamination in agricultural pro...Aspergillus species are ubiquitous fungi that produce mycotoxins(secondary metabolites)known as sterigmatocystin and aflatoxins in many different kinds of foods,which leads to serious contamination in agricultural products,thereby endangering human health.Extensive studies on Aspergillus fungi have been conducted on growth and development,aflatoxin biosynthesis,and their interactions with environment.Here,we summarized a series of functional genes of the main Aspergillus fungi relative to toxins occurrence in foods,which revealed the signal transduction mechanisms of their involvement in growth and development,toxin production,and response to light,anticipating providing theoretical guidance on developing control and prevention technologies for mycotoxin contamination in agricultural products to ensure food safety.展开更多
Chronic obstructive pulmonary disease(COPD),a disease responsible for early mortality worldwide,is well accepted to be associated with periodontitis epidemiologically.Although both of the diseases are the multi-microb...Chronic obstructive pulmonary disease(COPD),a disease responsible for early mortality worldwide,is well accepted to be associated with periodontitis epidemiologically.Although both of the diseases are the multi-microbial inflammatory disease,the precise underlying mechanisms by which periodontitis influences the progression of COPD remains largely unknown.Here,we established COPD accompanied with periodontitis mouse models and observed the pronounced progress in pulmonary symptoms and histopathology,cha racterized by poorer respiratory function,thicke ned bronchial walls,and increased neutrophils infiltration in lung tissue.Mechanistically,periodontitis pathogen Porphyromonas gingivalis(P.gingivalis)relocated in the lung through the respiratory tract and LPS from P.gingivalis promoted the secretion of chemokines CXCL2 and G-CSF of alveolar epithelial cells through NF-κB and p38 MAPK pathways to recruit neutrophils.Furthermore,exposure to P.gingivalis of infiltrated neutrophils released matrix metallopeptidase-8(MMP-8)and neutrophil elastase(NE),which aggravated airway inflammation and tissue damage.These findings indicated that periodontitis could exacerbate COPD via its pathogen P.gingivalis,which translocated in the lung and stimulated neutrophil chemotaxis and activation in the lung.展开更多
基金supported by the National Key R&D Program of China(No.2022YFE0204100)National Natural Science Foundation of China(Nos.12205067 and 12375199)the Fundamental Research Funds for the Central University(No.HIT.D?J.2023178)。
文摘This work continues the studies on searching for plasma media with the inverse electron energy distribution function(EEDF)and providing recommendations for setting up subsequent experiments.The inverse EEDF is a distribution function that increases with an increase in energy at zero electron energy.The inverse EEDF plays a central role in the problem of negative conductivity.Based on the previously obtained criterion for the formation of an inverse EEDF in a spatially inhomogeneous plasma,a heuristic method is proposed that allows one to avoid resource-intensive calculations for spatially two-dimensional(2D)kinetic modeling on a large array of different glow discharges.It is shown that the conditions for EEDF inversion can be realized in two-chamber discharge structures due to violating the known Boltzmann distribution for electron density.The theoretical conclusions are validated by numerical modeling of lowpressure two-chamber inductively-coupled plasma(ICP)discharges in the COMSOL Multiphysics environment.As a result,areas of conditions with inverse EEDF were found for subsequent detailed kinetic analysis and experimental studies.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金supported by grants from the Guangxi Science and Technology Major Project(GKAA24206023)the Biological Breeding-National Science and Technology Major Project(2024ZD04077)+2 种基金the National Natural Science Foundation of China(32272120)the National Key Research and Development Program of China(2024YFF1000800)the Guangdong Basic Research Center of Excellence for Precise Breeding of Future Crops Major Project(FCBRCE-202502,FCBRCE-202504).
文摘A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.
基金supported by the National Natural Science Foundation of China (No.82174074)。
文摘This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
文摘This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.
基金supported by CARS(CARS-21),the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-032)the Science and Technology Department of Xizang(XZ202401ZY0020)+2 种基金the Science and Technology Department of Sichuan Province(2023YFH0044,2023YFH0018)the Sichuan Province Science Foundation for Distinguished Young Scholars(2022JDJQ0006)the Doctoral Fund of Southwest University of Science and Technology(19ZX7117,21ZX7116).
文摘Saikosaponins are the major pharmacologically active components in Bupleurum genus and exhibit significant application potential in multiple fields such as immune regulation and anti-tumor activity.To elucidate the biosynthetic pathway of saikosaponins,we identified two cytochrome P450 monooxygenases,CYP716A41 and CYP716Y4,in Bupleurum chinense.These enzymes catalyze the C-28 oxidation and C-16 hydroxylation of oleanane-type triterpene skeletons,respectively.The catalytic efficiency of CYP716A41 from a southern B.chinense variety was significantly higher than that from a northern variety.Molecular docking and mutagenesis experiments revealed that amino acid residues at sites 9 and 35 may contribute to this difference in catalytic efficiency.Additionally,under cold stress,the expression levels of both CYP450 genes and the saikosaponin contents in the leaves of southern varieties were significantly higher compared to those in northern varieties.The variation in the catalytic efficiency of CYP716A41 and the differential expression of the two CYP450 genes under cold stress during winter are associated with the differences in saikosaponin biosynthesis in the leaves of southern and northern B.chinense varieties.This is consistent with the distinct medicinal usage practices observed between southern and northern China.
基金supported by the National Natural Science Foundation of China,Nos.82072165 and 82272256(both to XM)the Key Project of Xiangyang Central Hospital,No.2023YZ03(to RM)。
文摘Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.
文摘Nickel-catalyzed borylation of aryl nonaflates with B2pin2 could be realized,which proceeded effectively by means of C—O bond functionalization to afford a wide variety of valuable arylboronates in moderate to excellent yields with good functionality compatibility.In addition,the gram-scale synthesis and the application of the approach in the late-stage elaboration of aryl nonaflate derived from pterostilbene could also be achieved.
基金Supported in part by NSFC(Nos.12401011,12201214)National Key Research and Development Program of China(No.2021YFA1000700)+3 种基金Shaanxi Fundamental Science Research Project for Mathematics and Physics(No.23JSQ053)Science and Technology Program for Youth New Star of Shaanxi Province(No.2025ZC-KJXX-29)Natural Science Basic Research Program of Shaanxi Province(No.2025JC-YBQN-091)Scientific Research Foundation for Young Talents of WNU(No.2024XJ-QNRC-01)。
文摘Let K_(j)/Q,1≤j≤ν,ν≥2 be quadratic fields with pairwise coprime discriminants Dj,and let τ_(kj)^(K_(j))(n)be the divisor function associated to Dedekind zeta function SK_(j)(s).In this paper,we consider a multidimensional general divisor problem related to the τ_(kj)^(K_(j))(n)involving several number fields over square integers,by establishing the corresponding asymptotic formula.As an application,we also obtain the asymptotic formula of variance of these coefi icients.
基金supported by the NSFC(11561001)the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT18-A14)+4 种基金the NSF of Inner Mongolia(2022MS01004,2020MS01011)the Higher School Foundation of Inner Mongolia(NJZY20200)the Program for Key Laboratory Construction of Chifeng University(CFXYZD202004)the Research and Innovation Team of Complex Analysis and Nonlinear Dynamic Systems of Chifeng University(cfxykycxtd202005)the Youth Science Foundation of Chifeng University(cfxyqn202133).
文摘In recent years,researchers have extensively investigated the Hankel determinant,which consists of coefficients appearing in a holomorphic function’s Taylor-Maclaurin series.Hankel matrices are widely used in Markov processes,non-stationary signals,and other mathematical disciplines.The aim of the current research article is to first improve the bounds of coefficient-related problems by employing the well-known Carathéodory function.The problems that we are going to improve were obtained by Tang et al.The sharp estimates of the most difficult problem of geometric function theory known as the third-order Hankel determinant are also contributed here.Zalcman and Fekete-Szegöinequalities are also studied here for the defined family of holomorphic functions.
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金supported by National Natural Science Foundation of China[NO.11932013].
文摘This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.
基金Supported by The Natural Science Foundation of China,No.82374292the Plans for Major Provincial Science and Technology Projects of Anhui Province,No.202303a07020003the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine,No.ZYYCXTD-C-202401.
文摘Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金supported in part by the National Science Foundation under GrantsDMS 2436630 and 2436629.
文摘Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.
文摘Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios.
基金Project supported by the Fundamental Research Funds for the Central Universities(No.2042025kf0052)。
文摘In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the critical elementary reaction of molybdenum catalysis.However,the relevant density functional theory(DFT)studies are relatively scarce,especially regarding the rational selection of functionals.In this work,14 functionals were employed to investigate the Mo-catalyzed carbonyl oxidative addition step.A benchmark study was carried out to evaluate their performance in structure optimization and energy calculation.Analyses of mean absolute error(MAE)and mean squared error(MSE)indicated that the B3LYP-D3(BJ),TPSSh,and ωB97X-D functionals exhibited superior performance in structure optimization.Using the DLPNO-CCSD(T)functional as the reference,the M06,M06-L,and MN15-L functionals exhibited good performance for energy calculation based on the structures optimized using the B3LYP-D3(BJ)functional.In particular,MN15-L provided the best performance with the smallest MAE and MSE.
基金supported by the key project of National Natural Sciences Foundation of China(U22A20551,32030085)the Major Project of Hubei Hongshan Laboratory,China(2021hszd015)+2 种基金the Hubei Province Major Science and Technology Special Project,China(2023BBA002)the National Natural Sciences Foundation of China(U22A20551)the National Natural Science Foundation of China Excellent Youth Fund(32422072)。
文摘Aspergillus species are ubiquitous fungi that produce mycotoxins(secondary metabolites)known as sterigmatocystin and aflatoxins in many different kinds of foods,which leads to serious contamination in agricultural products,thereby endangering human health.Extensive studies on Aspergillus fungi have been conducted on growth and development,aflatoxin biosynthesis,and their interactions with environment.Here,we summarized a series of functional genes of the main Aspergillus fungi relative to toxins occurrence in foods,which revealed the signal transduction mechanisms of their involvement in growth and development,toxin production,and response to light,anticipating providing theoretical guidance on developing control and prevention technologies for mycotoxin contamination in agricultural products to ensure food safety.
基金supported by National High Level Hospital Clinical Research Funding,grant nos.BJ-2025-122,BJ2023-126CAMS Innovation Fund for Medical Sciences(CIFMS),grant no.2021-I2M-1050National Natural Science Foundation of China,grant no.82170956。
文摘Chronic obstructive pulmonary disease(COPD),a disease responsible for early mortality worldwide,is well accepted to be associated with periodontitis epidemiologically.Although both of the diseases are the multi-microbial inflammatory disease,the precise underlying mechanisms by which periodontitis influences the progression of COPD remains largely unknown.Here,we established COPD accompanied with periodontitis mouse models and observed the pronounced progress in pulmonary symptoms and histopathology,cha racterized by poorer respiratory function,thicke ned bronchial walls,and increased neutrophils infiltration in lung tissue.Mechanistically,periodontitis pathogen Porphyromonas gingivalis(P.gingivalis)relocated in the lung through the respiratory tract and LPS from P.gingivalis promoted the secretion of chemokines CXCL2 and G-CSF of alveolar epithelial cells through NF-κB and p38 MAPK pathways to recruit neutrophils.Furthermore,exposure to P.gingivalis of infiltrated neutrophils released matrix metallopeptidase-8(MMP-8)and neutrophil elastase(NE),which aggravated airway inflammation and tissue damage.These findings indicated that periodontitis could exacerbate COPD via its pathogen P.gingivalis,which translocated in the lung and stimulated neutrophil chemotaxis and activation in the lung.
