The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method...The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemio...Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.展开更多
The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase scree...The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.展开更多
It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cy...It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.展开更多
Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from nume...Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.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
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
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
We propose a methodology for testing two-sample means in high-dimensional functional data that requires no decaying pattern on eigenvalues of the functional data.To the best of our knowledge,we are the first to consid...We propose a methodology for testing two-sample means in high-dimensional functional data that requires no decaying pattern on eigenvalues of the functional data.To the best of our knowledge,we are the first to consider and address such a problem.To be specific,we devise a confidence region for the mean curve difference between two samples,which directly establishes a rigorous inferential procedure based on the multiplier bootstrap.In addition,the proposed test permits the functional observations in each sample to have mutually different distributions and arbitrary correlation structures,which is regarded as the desired property of distribution/correlation-free,leading to a more challenging scenario for theoretical development.Other desired properties include the allowance for highly unequal sample sizes,exponentially growing data dimension in sample sizes and consistent power behavior under fairly general alternatives.The proposed test is shown uniformly convergent to the prescribed significance,and its finite sample performance is evaluated via the simulation study and an application to electroencephalography data.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
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
基金supported by the National Social Science Foundation of China under Grant No.23&ZD126National Science Foundation of China under Grant No.12471256+1 种基金Natural Science Foundation of Shanxi Province under Grant No.202203021221219Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under Grant No.2023L164。
文摘The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
基金supported in part by the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.82304253)(and 82273709)the Foundation for Young Talents in Higher Education of Guangdong Province(Grant No.2022KQNCX021)the PhD Starting Project of Guangdong Medical University(Grant No.GDMUB2022054).
文摘Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
基金supported by the Project of the Hubei Provincial Department of Science and Technology(Grant Nos.2022CFB957,2022CFB475)the National Natural Science Foundation of China(Grant No.11847118)。
文摘The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.
基金Supported by the National Natural Science Foundation of China(12201446)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB110005)the Shuangchuang Program of Jiangsu Province(JSSCBS20220898)。
文摘It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.
基金supported by Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021212254,202403021221109)Graduate Research Innovation Project in Shanxi Province(No.2024KY616).
文摘Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.
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.
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 National Natural Science Foundation of China (Grant No.11901313)Fundamental Research Funds for the Central Universities+1 种基金Key Laboratory for Medical Data Analysis and Statistical Research of TianjinKey Laboratory of Pure Mathematics and Combinatorics.
文摘We propose a methodology for testing two-sample means in high-dimensional functional data that requires no decaying pattern on eigenvalues of the functional data.To the best of our knowledge,we are the first to consider and address such a problem.To be specific,we devise a confidence region for the mean curve difference between two samples,which directly establishes a rigorous inferential procedure based on the multiplier bootstrap.In addition,the proposed test permits the functional observations in each sample to have mutually different distributions and arbitrary correlation structures,which is regarded as the desired property of distribution/correlation-free,leading to a more challenging scenario for theoretical development.Other desired properties include the allowance for highly unequal sample sizes,exponentially growing data dimension in sample sizes and consistent power behavior under fairly general alternatives.The proposed test is shown uniformly convergent to the prescribed significance,and its finite sample performance is evaluated via the simulation study and an application to electroencephalography data.
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.
