High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in avia...High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in aviation industry.However,existing researches generally focuses on the effect of a single parameter while lacks multivariable researches.Considering of the fact that gap and misalignment are inevitable in real part clamping,adaptive intelligent welding is usually used during automatic manufacturing,which means under the control of filler wire amount per length of a weld,other parameters including current,welding speed and wire feed speed during one single weld are changing according to the specific clamping situation.Therefore,the influence of specific energy input led by different welding parameters within one adaptive welding program on microstructure and mechanical property of the weld needs to be clarified.This study investigates the effect of welding heat input(ranging from 1048.3 J/mm to 825.6 J/mm within one adaptive welding program control)on the formation quality of 3.25 mm thick 6061 aluminum alloy joints fabricated by HFP-GTAW with 4043 filler wire.According to the obtained results,non-monotonic relationship between heat input and porosity,with an optimal minimum of 4.92%achieved at an intermediate heat input of 856.8 J/mm.The 21.2%decrease of energy input during welding process would reduce the average grain size in the weld center and adjacent to fusion line by 18.6%and 19.4%,respectively.The ratios between fluctuation range to minimum value in average yield and the relative ranges of yield strength and ultimate tensile strength across the tested heat inputs were 14.7%and 12.7%,respectively.The findings provide a general overview on how the microstructure and mechanical properties would fluctuate in an adaptively controlled HFP-GTAW fabricated aluminum alloy weld.展开更多
The efficient extraction and separation of valuable metal elements from coal gasification fine slag(CGFS)are crucial for the comprehensive high-value utilization of its constituents.This study focused on the carbon-ri...The efficient extraction and separation of valuable metal elements from coal gasification fine slag(CGFS)are crucial for the comprehensive high-value utilization of its constituents.This study focused on the carbon-rich components of CGFS(CGFS-H)and systematically investigates the selective leaching behavior of Fe^(3+),Al^(3+)and Ca^(2+)using three organic acid extractants,i.e.,citric acid,tartaric acid,and tetrasodium iminodisuccinate.Additionally,the stepwise leaching of iron,aluminum and calcium from CGFS-H is explored.The selective dissolution mechanisms of these metals by different organic acids are elucidated through X-ray diffraction(XRD),X-ray fluorescence(XRF),and scanning electron microscopy(SEM)analyses.The results indicate that tetrasodium iminodisuccinate exhibits the highest leaching selectivity for Fe^(3+),while tartaric acid demonstrateds a comparable affinity for both Fe^(3+)and Al^(3+).In contrast citric acid shows superior selectivity toward Ca^(2+).The leaching yield of Fe^(3+),Al^(3+)and Ca^(2+)after sequential leaching with the three organic acids were 79.8%,65.08%and 78.6%,respectively.These findings confirm that effective and selective separation of Fe^(3+),Al^(3+)and Ca^(2+)from CGFS-H can be achieved via optimized organic acid-based leaching strategies.This advancement provides a critical foundation for developing Ca/Fe/Al hydrotalcite materials using CGFS-H as a sustainable feedstock,thereby facilitating the transformation of waste residue into high-value functional materials and promoting resourceefficient utilization of coal gasification fine slag.展开更多
Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive di...Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.展开更多
[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromat...[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromatography.Their chemical structures were elucidated on the basis of physicochemical properties and spectral data.[Results]Five compounds were isolated and identified as:di(2-ethylhexyl)phthalate(1),cinnamic anhydride(2),phthalic acid(3),citric acid(4),and syringaldehyde(5).[Conclusions]All compounds were isolated from this plant for the first time.展开更多
There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution...There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.展开更多
Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding...Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.展开更多
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate...Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.展开更多
The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and propertie...The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and properties of AM60 based alloys.AM60-0.2La alloy showed excellent mechanical properties.The ultimate tensile strength,yield strength,and elongation of(288.0±1.7)MPa,(158.0±1.0)MPa,and(22.0±3.0)%were achieved in AM60-0.2La alloy.Besides,AM60-0.2La alloy exhibited the best corrosion resistance(0.29 mm/a)and fluidity among the investigated four alloys.The excellent mechanical properties and corrosion resistance are mainly attributed to the grain refinement strengthening,low porosity,and low content of large shrinkage porosity,promising for super-sized integrated automotive components.展开更多
Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i....Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.展开更多
In this paper,a fast step heterodyne light-induced thermoelastic spectroscopy(SH-LITES)sensor using a high-frequency quartz tuning fork(QTF)with resonant frequency of~100 kHz is reported for the first time.The theoret...In this paper,a fast step heterodyne light-induced thermoelastic spectroscopy(SH-LITES)sensor using a high-frequency quartz tuning fork(QTF)with resonant frequency of~100 kHz is reported for the first time.The theoretical principle of heterodyne LITES(H-LITES)signal generation is analyzed firstly,and an acetylene(C_(2)H_(2))H-LITES sensor is established to verify its performance.Experimental comparisons between the high-frequency QTF and a standard commercial QTF with resonant frequency of~32.768 kHz reveal that the high-frequency QTF exhibits a tenfold faster response time.Specifically,the H-LITES sensor with this QTF achieves a 33 ms measurement cycle,90%shorter than commercial counterparts.Furthermore,The SH-LITES technique is proposed to further shorten the scanning time to 15 ms,which achieves the shortest LITES measurement time known to date.To demonstrate its advantages in dynamic gas detection,an H_(2)O-LITES system integrating both QTF types is constructed for real-time monitoring of H_(2)O concentration during different respiration patterns.Comparative measurements show that the SH-LITES more accurately captures dynamic H_(2)O concentration fluctuations during respiration,outperforming the commercial QTF-based H-LITES sensor in rapid response scenarios.展开更多
To investigate the evolution of grain orientation and slip modes in magnesium alloys with multiple texture components,an AZ31 gradient-structured magnesium alloy sheet was fabricated using hard plate rolling(HPR).The ...To investigate the evolution of grain orientation and slip modes in magnesium alloys with multiple texture components,an AZ31 gradient-structured magnesium alloy sheet was fabricated using hard plate rolling(HPR).The changes in texture and slip modes under different reductions were examined.The results demonstrate that the AZ31 magnesium alloy sheets display a self-epitaxial gradient structure,with the best mechanical properties observed at rolling temperature of 673 K and reduction of 50%.Significant changes in texture type and strength are observed along the normal direction(ND)of the sheet.The coarse-grain region exhibits a bimodal texture aligned with the rolling direction.These texture variations enhance the stress distribution at the fine grain-coarse grain interface,influencing the grain orientation and the activation of different slip modes,thus improving the mechanical properties of gradient-structured magnesium alloy sheets.This approach offers a new strategy for the fabrication of high-performance magnesium alloy sheets.展开更多
China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and dis...China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.展开更多
Acupuncture,a therapeutic practice rooted in traditional Chinese medicine and integrated with modern neuroscience,achieves its effects by stimulating sensory nerves at specific anatomical points known as acupoints.Thi...Acupuncture,a therapeutic practice rooted in traditional Chinese medicine and integrated with modern neuroscience,achieves its effects by stimulating sensory nerves at specific anatomical points known as acupoints.This review systematically explores the therapeutic components of acupuncture,emphasizing the interplay between sensory nerve characteristics and neural signaling pathways.Key factors such as acupoint location,needling depth,stimulation intensity,retention time,and the induction of sensations(e.g.,Deqi)are analyzed for their roles in neural activation and clinical outcomes.The review highlights how variations in spinal segment targeting,tissue-specific receptor activation,and stimulation modalities(e.g.,manual acupuncture,electroacupuncture,moxibustion)influence therapeutic effects.Emerging evidence underscores the significance of ion channels,dermatomes,myotomes,and genespecific pathways in mediating systemic effects.Additionally,the differential roles of mechanical,thermal and nociceptive stimuli and the temporal dynamics of sensory and immune responses are addressed.While insights from animal models have advanced our understanding,their translation to clinical practice requires further investigation.This comprehensive review identifies critical parameters for optimizing acupuncture therapy,advocating for individualized treatment strategies informed by neuroanatomical and neurophysiological principles,ultimately enhancing its precision and efficacy in modern medicine.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC...Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.展开更多
AIM To investigate the material basis and mechanism underlying the therapeutic effect of DLC in T2DM.METHODS T2DM was triggered in rats using a high-sugar,high-fat diet alongside 35 mg/kg streptozotocin.The effect of ...AIM To investigate the material basis and mechanism underlying the therapeutic effect of DLC in T2DM.METHODS T2DM was triggered in rats using a high-sugar,high-fat diet alongside 35 mg/kg streptozotocin.The effect of DLC on the intestinal microbiota in T2DM rats was analyzed via 16S rDNA sequencing.Targeted metabolomics was conducted to evaluate the impact of DLC on the levels of nine short-chain fatty acids(SCFAs).Untargeted metabolomics examined DLC-induced alterations in fecal metabolites and associated metabolic pathways.Additionally,Spearman’s correlation analysis assessed gut microbiota and fecal metabolite relationships.RESULTS DLC significantly attenuated pathological weight loss,reduced fasting blood glucose levels,restored blood sugar homeostasis,and ameliorated dyslipidemia in T2DM rats.The 16S rDNA sequencing revealed that DLC enhanced microbial diversity and reversed intestinal dysbiosis.Targeted metabolomics indicated decreased acetic acid and propionic acid levels and increased butyric acid,isobutyric acid,and 2-methylbutyric acid levels after DLC treatment.Untargeted metabolomics revealed 57 metabolites with altered expression associated with amino acid,carbohydrate,purine,and biotin pathways.The Spearman analysis demonstrated significant links between specific gut microbiota taxa and fecal metabolites.CONCLUSION DLC may exert hypoglycemic effects by modulating intestinal flora genera,SCFA levels,and fecal metabolites.展开更多
Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants h...Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.展开更多
Dear Editor,Local recurrence and cervical lymph node metastases are major causes of mortality in patients with head and neck squamous cell carcinoma(HNSCC).To date,none of the proposed strategies for predicting outcom...Dear Editor,Local recurrence and cervical lymph node metastases are major causes of mortality in patients with head and neck squamous cell carcinoma(HNSCC).To date,none of the proposed strategies for predicting outcomes in this disease have proven fully effective,and a comprehensive physical examination remains the primary method for early detection and monitoring of HNSCC.展开更多
基金supported by the Commercial Aircraft Corporation of China Ltd.(Grant No.COMAC-SFGS-2024–569)Fundamental Research Funds for the Central Universities and Institute of Marine Equipment,Shanghai Rising-Star Program of Science and Technology Commission of Shanghai Municipality(Grant No.23QA1404700)+1 种基金National Natural Science Foundation of China(Grant No.52475384,52505409)China Postdoctoral Science Foundation(Grant No.2024M761963)。
文摘High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in aviation industry.However,existing researches generally focuses on the effect of a single parameter while lacks multivariable researches.Considering of the fact that gap and misalignment are inevitable in real part clamping,adaptive intelligent welding is usually used during automatic manufacturing,which means under the control of filler wire amount per length of a weld,other parameters including current,welding speed and wire feed speed during one single weld are changing according to the specific clamping situation.Therefore,the influence of specific energy input led by different welding parameters within one adaptive welding program on microstructure and mechanical property of the weld needs to be clarified.This study investigates the effect of welding heat input(ranging from 1048.3 J/mm to 825.6 J/mm within one adaptive welding program control)on the formation quality of 3.25 mm thick 6061 aluminum alloy joints fabricated by HFP-GTAW with 4043 filler wire.According to the obtained results,non-monotonic relationship between heat input and porosity,with an optimal minimum of 4.92%achieved at an intermediate heat input of 856.8 J/mm.The 21.2%decrease of energy input during welding process would reduce the average grain size in the weld center and adjacent to fusion line by 18.6%and 19.4%,respectively.The ratios between fluctuation range to minimum value in average yield and the relative ranges of yield strength and ultimate tensile strength across the tested heat inputs were 14.7%and 12.7%,respectively.The findings provide a general overview on how the microstructure and mechanical properties would fluctuate in an adaptively controlled HFP-GTAW fabricated aluminum alloy weld.
基金Supported by National Natural Science Foundation(52374279)。
文摘The efficient extraction and separation of valuable metal elements from coal gasification fine slag(CGFS)are crucial for the comprehensive high-value utilization of its constituents.This study focused on the carbon-rich components of CGFS(CGFS-H)and systematically investigates the selective leaching behavior of Fe^(3+),Al^(3+)and Ca^(2+)using three organic acid extractants,i.e.,citric acid,tartaric acid,and tetrasodium iminodisuccinate.Additionally,the stepwise leaching of iron,aluminum and calcium from CGFS-H is explored.The selective dissolution mechanisms of these metals by different organic acids are elucidated through X-ray diffraction(XRD),X-ray fluorescence(XRF),and scanning electron microscopy(SEM)analyses.The results indicate that tetrasodium iminodisuccinate exhibits the highest leaching selectivity for Fe^(3+),while tartaric acid demonstrateds a comparable affinity for both Fe^(3+)and Al^(3+).In contrast citric acid shows superior selectivity toward Ca^(2+).The leaching yield of Fe^(3+),Al^(3+)and Ca^(2+)after sequential leaching with the three organic acids were 79.8%,65.08%and 78.6%,respectively.These findings confirm that effective and selective separation of Fe^(3+),Al^(3+)and Ca^(2+)from CGFS-H can be achieved via optimized organic acid-based leaching strategies.This advancement provides a critical foundation for developing Ca/Fe/Al hydrotalcite materials using CGFS-H as a sustainable feedstock,thereby facilitating the transformation of waste residue into high-value functional materials and promoting resourceefficient utilization of coal gasification fine slag.
基金Supported by National Natural Science Foundation of China(U24B6018,22178243)。
文摘Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.
基金Supported by Jiangxi Education Department Project(GJJ201533)University-level Project of Gannan Medical University(YB201902).
文摘[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromatography.Their chemical structures were elucidated on the basis of physicochemical properties and spectral data.[Results]Five compounds were isolated and identified as:di(2-ethylhexyl)phthalate(1),cinnamic anhydride(2),phthalic acid(3),citric acid(4),and syringaldehyde(5).[Conclusions]All compounds were isolated from this plant for the first time.
基金supported by the National Nature Science Foundation of China(Nos.12027901 and 12041202)Synchrotron Radiation Joint Fund of University of Science and Technology of China(Nos.KY2090000059 and KY2090000054)。
文摘There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant No.42407221)the Open Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Grant No.SKLGP2024K009)the Hubei Provincial Natural Science Foun-dation,China(Grant No.2023AFB567).
文摘Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.
基金financially supported by the National Key Research and Development Program of China(Nos.2022YFB3709300,2021YFB3701000)the National Natural Science Foundation of China(Nos.52271090,52071036,U2037601,U21A2048)+1 种基金Chongqing Science and Technology Commission,China(Nos.CSTB2022TIAD-KPX0021,CSTC2024YCJHBGZXM0164,CSTB2024TIAD-KPX0001)the Fundamental Research Funds for the Central Universities,China(No.2022CDJDX-002)。
文摘The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and properties of AM60 based alloys.AM60-0.2La alloy showed excellent mechanical properties.The ultimate tensile strength,yield strength,and elongation of(288.0±1.7)MPa,(158.0±1.0)MPa,and(22.0±3.0)%were achieved in AM60-0.2La alloy.Besides,AM60-0.2La alloy exhibited the best corrosion resistance(0.29 mm/a)and fluidity among the investigated four alloys.The excellent mechanical properties and corrosion resistance are mainly attributed to the grain refinement strengthening,low porosity,and low content of large shrinkage porosity,promising for super-sized integrated automotive components.
基金supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101037424.
文摘Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.
基金financial supports from the National Natural Science Foundation of China(Grant No.62335006,62275065,624B2050,62022032,and 62405078)Open Subject of Hebei Key Laboratory of Advanced Laser Technology and Equipment(HBKL-ALTE2025001)+2 种基金Heilongjiang Postdoctoral Fund(Grant No.LBH-Z23144 and LBH-Z24155)Natural Science Foundation of Heilongjiang Province(Grant No.LH2024F031)China Postdoctoral Science Foundation(Grant No.2024M764172).
文摘In this paper,a fast step heterodyne light-induced thermoelastic spectroscopy(SH-LITES)sensor using a high-frequency quartz tuning fork(QTF)with resonant frequency of~100 kHz is reported for the first time.The theoretical principle of heterodyne LITES(H-LITES)signal generation is analyzed firstly,and an acetylene(C_(2)H_(2))H-LITES sensor is established to verify its performance.Experimental comparisons between the high-frequency QTF and a standard commercial QTF with resonant frequency of~32.768 kHz reveal that the high-frequency QTF exhibits a tenfold faster response time.Specifically,the H-LITES sensor with this QTF achieves a 33 ms measurement cycle,90%shorter than commercial counterparts.Furthermore,The SH-LITES technique is proposed to further shorten the scanning time to 15 ms,which achieves the shortest LITES measurement time known to date.To demonstrate its advantages in dynamic gas detection,an H_(2)O-LITES system integrating both QTF types is constructed for real-time monitoring of H_(2)O concentration during different respiration patterns.Comparative measurements show that the SH-LITES more accurately captures dynamic H_(2)O concentration fluctuations during respiration,outperforming the commercial QTF-based H-LITES sensor in rapid response scenarios.
基金supported by the Natural Science Foundation of Heilongjiang Province,China(No.JQ2022E004)。
文摘To investigate the evolution of grain orientation and slip modes in magnesium alloys with multiple texture components,an AZ31 gradient-structured magnesium alloy sheet was fabricated using hard plate rolling(HPR).The changes in texture and slip modes under different reductions were examined.The results demonstrate that the AZ31 magnesium alloy sheets display a self-epitaxial gradient structure,with the best mechanical properties observed at rolling temperature of 673 K and reduction of 50%.Significant changes in texture type and strength are observed along the normal direction(ND)of the sheet.The coarse-grain region exhibits a bimodal texture aligned with the rolling direction.These texture variations enhance the stress distribution at the fine grain-coarse grain interface,influencing the grain orientation and the activation of different slip modes,thus improving the mechanical properties of gradient-structured magnesium alloy sheets.This approach offers a new strategy for the fabrication of high-performance magnesium alloy sheets.
文摘China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2020R1C1C1004107)。
文摘Acupuncture,a therapeutic practice rooted in traditional Chinese medicine and integrated with modern neuroscience,achieves its effects by stimulating sensory nerves at specific anatomical points known as acupoints.This review systematically explores the therapeutic components of acupuncture,emphasizing the interplay between sensory nerve characteristics and neural signaling pathways.Key factors such as acupoint location,needling depth,stimulation intensity,retention time,and the induction of sensations(e.g.,Deqi)are analyzed for their roles in neural activation and clinical outcomes.The review highlights how variations in spinal segment targeting,tissue-specific receptor activation,and stimulation modalities(e.g.,manual acupuncture,electroacupuncture,moxibustion)influence therapeutic effects.Emerging evidence underscores the significance of ion channels,dermatomes,myotomes,and genespecific pathways in mediating systemic effects.Additionally,the differential roles of mechanical,thermal and nociceptive stimuli and the temporal dynamics of sensory and immune responses are addressed.While insights from animal models have advanced our understanding,their translation to clinical practice requires further investigation.This comprehensive review identifies critical parameters for optimizing acupuncture therapy,advocating for individualized treatment strategies informed by neuroanatomical and neurophysiological principles,ultimately enhancing its precision and efficacy in modern medicine.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金funding from the National Natural Science Foundation of China (Grant No.42277175)the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056)the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).
文摘Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.
基金Supported by the National Natural Science Foundation of China,No.82160771NATCM's Project of High-Level Construction of Key TCM Disciplines:Traditional Medicine of Chinese Minority(Zhuang Medicine),No.zyyzdxk-2023165+7 种基金Guangxi One Thousand Young and Middle-Aged College and University Backbones Teachers Cultivation Program,No.[2019]5Guangxi Traditional Chinese Medicine Multidisciplinary Cross Innovation Team Project,No.GZKJ2309Guangxi Key R&D Plan Project,No.AB21196016Guangxi Key Discipline of Traditional Chinese Medicine Zhuang Pharmacy,No.GZXK-Z-20-64The First-Class Subject of Traditional Chinese Medicine(Ethnic Pharmacy)in Guangxi,No.[2018]12Guangxi Science and Technology Base and Talent Special Project,No.AD20238058 and No.AD21238031the Third Batch of Cultivating High-level Talent Teams in the“Qi Huang Project”of the Guangxi University of Chinese Medicine,No.202406and Huang Danian Style Teacher Team From Universities in Guangxi Zhuang Autonomous Region“Traditional Chinese Medicine Inheritance and Innovation Teacher Team”,No.[2023]31.
文摘AIM To investigate the material basis and mechanism underlying the therapeutic effect of DLC in T2DM.METHODS T2DM was triggered in rats using a high-sugar,high-fat diet alongside 35 mg/kg streptozotocin.The effect of DLC on the intestinal microbiota in T2DM rats was analyzed via 16S rDNA sequencing.Targeted metabolomics was conducted to evaluate the impact of DLC on the levels of nine short-chain fatty acids(SCFAs).Untargeted metabolomics examined DLC-induced alterations in fecal metabolites and associated metabolic pathways.Additionally,Spearman’s correlation analysis assessed gut microbiota and fecal metabolite relationships.RESULTS DLC significantly attenuated pathological weight loss,reduced fasting blood glucose levels,restored blood sugar homeostasis,and ameliorated dyslipidemia in T2DM rats.The 16S rDNA sequencing revealed that DLC enhanced microbial diversity and reversed intestinal dysbiosis.Targeted metabolomics indicated decreased acetic acid and propionic acid levels and increased butyric acid,isobutyric acid,and 2-methylbutyric acid levels after DLC treatment.Untargeted metabolomics revealed 57 metabolites with altered expression associated with amino acid,carbohydrate,purine,and biotin pathways.The Spearman analysis demonstrated significant links between specific gut microbiota taxa and fecal metabolites.CONCLUSION DLC may exert hypoglycemic effects by modulating intestinal flora genera,SCFA levels,and fecal metabolites.
基金supported by the key project at the central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(Grant number 2060302)the National Natural Science Foundation of China(Grant number 82373982,82173929).
文摘Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.
文摘Dear Editor,Local recurrence and cervical lymph node metastases are major causes of mortality in patients with head and neck squamous cell carcinoma(HNSCC).To date,none of the proposed strategies for predicting outcomes in this disease have proven fully effective,and a comprehensive physical examination remains the primary method for early detection and monitoring of HNSCC.
