Retinoic acid(RA),the active metabolite of vitamin A(the retinoids),elicits a wide spectrum of biological activities critical to the development and health of most of the organ systems including the nervous systems(Co...Retinoic acid(RA),the active metabolite of vitamin A(the retinoids),elicits a wide spectrum of biological activities critical to the development and health of most of the organ systems including the nervous systems(Corcoran et al.,2002).The effects of RA are mediated by two very distinct pathways;the first is manifested in the nucleus by binding to a large family of nuclear RA receptors(RARs)to regulate proper expression of RAtargeted genes.展开更多
Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3...Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.展开更多
Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consi...Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.展开更多
Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
Piling Canon refers to a woodblock-printed Chinese Buddhist Canon during the late Qing Dynasty.Despite its historical significance,it has received limited attention from the academia,as its discovery took place after ...Piling Canon refers to a woodblock-printed Chinese Buddhist Canon during the late Qing Dynasty.Despite its historical significance,it has received limited attention from the academia,as its discovery took place after the turn of the 21st century.This study explores the background,supervisor,proofreader,engravers,donors,and other factors that contributed to the publication of the Piling Canon.It was supervised by Buddhist monk Qingrong in Changzhou Tianning Monastery from 1908 to 1926,due to the commission of Yang Wenhui.By investigating the historical records in the colophons of Piling Canon,we found that engraving locations are distributed in Hubei,Yangzhou,and Danyang which engravers operated in groups;the majority of donors were found to be individuals and group forms,social fundraising was included as well.It is noteworthy that Sheng Xuanhuai made a significant contribution in terms of funding.Furthermore,the production of the Piling Canon confirms to the commence of Buddhism revival,as Buddhist scriptures in Jiangnan regions were almost destroyed after the Taiping Rebellion.The research shed light on extensive participation of cultural celebrities,diverse donation forms,and excellent engraving,offering a vivid depiction of Buddhist belief and social landscape in Jiangnan region.展开更多
The aim was to clarify the environmental driving factors of soil fertility indicators in artificial forests of Guangxi and comprehensively evaluate the soil fertility level.By collecting data on the current status of ...The aim was to clarify the environmental driving factors of soil fertility indicators in artificial forests of Guangxi and comprehensively evaluate the soil fertility level.By collecting data on the current status of soil in artificial forests,the spatial distribution of major soil fertility indicators was analyzed,and the distribution map of the fertility index of artificial forests in the entire region and the comprehensive fertility index of artificial forests of different soil types were obtained.Canonical correspondence analysis method was used to analyze soil fertility indicators and environmental factors,and the environmental driving factors of soil fertility indicators for artificial forests of the main soil types in Guangxi were obtained.The results showed that over 90%of the soil fertility index of artificial forests in the entire region was between 0.20 and 0.50.The order of soil fertility index of different soil types of artificial forests from high to low was yellow brown soil>yellow red soil>yellow soil>red soil>limestone soil>latosolic red soil>laterite.In artificial forests of latosolic red soil,the correlation between soil alkaline nitrogen and organic matter,annual average temperature was high,while the correlation between soil available phosphorus and organic matter,pH was high,and the correlation between soil available potassium and environmental factors such as slope,altitude,rainfall,accumulated temperature,and slope aspect was high.In artificial forests of red soil,the correlation between soil alkaline nitrogen and slope,altitude was high,while the correlation between soil available phosphorus and accumulated temperature,rainfall was high,and the correlation between soil available potassium and pH was high.In artificial forests of limestone soil,there was a high correlation between soil alkaline nitrogen and slope,organic matter,a high correlation between soil available phosphorus and accumulated temperature,rainfall,and a high correlation between soil available potassium and pH.展开更多
Skeletal muscles are essential for locomotion,posture,and metabolic regulation.To understand physiological processes,exercise adaptation,and muscle-related disorders,it is critical to understand the molecular pathways...Skeletal muscles are essential for locomotion,posture,and metabolic regulation.To understand physiological processes,exercise adaptation,and muscle-related disorders,it is critical to understand the molecular pathways that underlie skeletal muscle function.The process of muscle contra ction,orchestrated by a complex interplay of molecular events,is at the core of skeletal muscle function.Muscle contraction is initiated by an action potential and neuromuscular transmission requiring a neuromuscular junction.Within muscle fibers,calcium ions play a critical role in mediating the interaction between actin and myosin filaments that generate force.Regulation of calcium release from the sarcoplasmic reticulum plays a key role in excitation-contraction coupling.The development and growth of skeletal muscle are regulated by a network of molecular pathways collectively known as myogenesis.Myogenic regulators coordinate the diffe rentiation of myoblasts into mature muscle fibers.Signaling pathways regulate muscle protein synthesis and hypertrophy in response to mechanical stimuli and nutrient availability.Seve ral muscle-related diseases,including congenital myasthenic disorders,sarcopenia,muscular dystrophies,and metabolic myopathies,are underpinned by dys regulated molecular pathways in skeletal muscle.Therapeutic interventions aimed at preserving muscle mass and function,enhancing regeneration,and improving metabolic health hold promise by targeting specific molecular pathways.Other molecular signaling pathways in skeletal muscle include the canonical Wnt signaling pathway,a critical regulator of myogenesis,muscle regeneration,and metabolic function,and the Hippo signaling pathway.In recent years,more details have been uncovered about the role of these two pathways during myogenesis and in developing and adult skeletal muscle fibers,and at the neuromuscular junction.In fact,research in the last few years now suggests that these two signaling pathways are interconnected and that they jointly control physiological and pathophysiological processes in muscle fibers.In this review,we will summarize and discuss the data on these two pathways,focusing on their concerted action next to their contribution to skeletal muscle biology.However,an in-depth discussion of the noncanonical Wnt pathway,the fibro/a dipogenic precursors,or the mechanosensory aspects of these pathways is not the focus of this review.展开更多
Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on fe...Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.展开更多
Decision implication is a form of decision knowledge represen-tation,which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes.Compared with other...Decision implication is a form of decision knowledge represen-tation,which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes.Compared with other forms of decision knowledge representation,decision implication has a stronger knowledge representation capability.Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance.Decision implication canonical basis(DICB)is the most compact set of decision implications,which can efficiently represent all knowledge in the decision context.In order to mine all deci-sion information on decision context under attribute granulating,this paper proposes an updated method of DICB.To this end,the paper reduces the update of DICB to the updates of decision premises after deleting an attribute and after adding granulation attributes of some attributes.Based on this,the paper analyzes the changes of decision premises,examines the properties of decision premises,designs an algorithm for incrementally generating DICB,and verifies its effectiveness through experiments.In real life,by using the updated algorithm of DICB,users may obtain all decision knowledge on decision context after attribute granularization.展开更多
BACKGROUND Members of the transient receptor potential(TRP)protein family shape oncogenic development,but the specific relevance of TRP-related genes in hepatocellular carcinoma(HCC)has yet to be defined.AIM To invest...BACKGROUND Members of the transient receptor potential(TRP)protein family shape oncogenic development,but the specific relevance of TRP-related genes in hepatocellular carcinoma(HCC)has yet to be defined.AIM To investigate the role of TRP genes in HCC,their association with HCC development and treatment was examined.METHODS HCC patient gene expression and clinical data were downloaded from The Cancer Genome Atlas database,and univariate and least absolute shrinkage and selection operator Cox regression models were employed to explore the TRP-related risk spectrum.Based on these analyses,clinically relevant TRP family genes were selected,and the association between the key TRP canonical type 1(TRPC1)gene and HCC patient prognosis was evaluated.RESULTS In total,28 TRP family genes were screened for clinical relevance,with multivariate analyses ultimately revealing three of these genes(TRPC1,TRP cation channel subfamily M member 2,and TRP cation channel subfamily M member 6)to be significantly associated with HCC patient prognosis(P<0.05).These genes were utilized to establish a TRP-related risk model.Patients were separated into low-and high-risk groups based on the expression of these genes,and high-risk patients exhibited a significantly poorer prognosis(P=0.001).Functional analyses highlighted pronounced differences in the immune status of patients in these two groups and associated enriched immune pathways.TRPC1 was identified as a candidate gene in this family worthy of further study,with HCC patients expressing higher TRPC1 levels exhibiting poorer survival outcomes.Consistently,quantitative,immunohistochemistry,and western blot analyses revealed increased TRPC1 expression in HCC.CONCLUSION These three TRP genes help determine HCC patient prognosis,providing insight into tumor immune status and immunological composition.These findings will help design combination therapies including immunotherapeutic and anti-TRP agents.展开更多
Globally known about the drivers through which farmers are instigated to uphold and use Hail Canon Technology(HCT)is lacking.Therefore,this article intended to examine the drivers of forecasting the behavioral intenti...Globally known about the drivers through which farmers are instigated to uphold and use Hail Canon Technology(HCT)is lacking.Therefore,this article intended to examine the drivers of forecasting the behavioral intention and acceptance behavior of the HCT,249 apple farmers from northwestern Iran were recruited,including adopters(n1=114)and non-adopters(n2=135).The conceptual foundation included demographic theory,resource-based theory,theory of planned behavior,innovation diffusion model,and institutional support model.We also used the system dynamics model(SDM)in the Netlogo to assess the results of the conventional statistical approach(i.e.,the logistic model).Authenticated the fitness of conceptual model with the data,logistic model manifests that the most outstanding determinants of the acceptance of HCT entail age,experience,total land size,income,attitude,compatibility,visibility,relative advantage,and financial support.Using the SDM,it was also shown that the results of the logistic model are confirmed by the SDM.In conclusion,management implications are available for the university extension to eliminate the adoption obstacles and stir up farmers to join in applying HCT,furthermore,researchers would avail themselves of remarks for future research.展开更多
Dear Editor,A global and local canonical correlation analysis(GLCCA)based on data-driven is presented for underwater positioning.Underwater positioning technology can help the underwater targets move predetermined des...Dear Editor,A global and local canonical correlation analysis(GLCCA)based on data-driven is presented for underwater positioning.Underwater positioning technology can help the underwater targets move predetermined destinations for specific tasks[1].Since using different sensor,underwater positioning can be divided into three types:inertial navigation,hydroacoustic positioning and geophysical navigation.展开更多
Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the ...Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.展开更多
Grand canonical Monte Carlo simulation(GCMCs)is utilized for studying hydrogen storage gravimetric density by pha-graphene at different metal densities,temperatures and pressures.It is demonstrated that the optimum ad...Grand canonical Monte Carlo simulation(GCMCs)is utilized for studying hydrogen storage gravimetric density by pha-graphene at different metal densities,temperatures and pressures.It is demonstrated that the optimum adsorbent location for Li atoms is the center of the seven-membered ring of pha-graphene.The binding energy of Li-decorated phagraphene is larger than the cohesive energy of Li atoms,implying that Li can be distributed on the surface of pha-graphene without forming metal clusters.We fitted the force field parameters of Li and C atoms at different positions and performed GCMCs to study the absorption capacity of H_(2).The capacity of hydrogen storage was studied by the differing density of Li decoration.The maximum hydrogen storage capacity of 4Li-decorated pha-graphene was 15.88 wt%at 77 K and100 bar.The enthalpy values of adsorption at the three densities are in the ideal range of 15 kJ·mol^(-1)-25 kJ·mol^(-1).The GCMC results at different pressures and temperatures show that with the increase in Li decorative density,the hydrogen storage gravimetric ratio of pha-graphene decreases but can reach the 2025 US Department of Energy's standard(5.5 wt%).Therefore,pha-graphene is considered to be a potential hydrogen storage material.展开更多
基金supported by NIH research grants NS132277 and DK60521。
文摘Retinoic acid(RA),the active metabolite of vitamin A(the retinoids),elicits a wide spectrum of biological activities critical to the development and health of most of the organ systems including the nervous systems(Corcoran et al.,2002).The effects of RA are mediated by two very distinct pathways;the first is manifested in the nucleus by binding to a large family of nuclear RA receptors(RARs)to regulate proper expression of RAtargeted genes.
基金funded by the Research Foundation of Education Bureau of Hunan Province,China,under Grant Number 21B0060the National Natural Science Foundation of China,under Grant Number 61701179.
文摘Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.
基金NationalNatural Science Foundation of China,Grant/AwardNumber:61867004National Natural Science Foundation of China Youth Fund,Grant/Award Number:41801288.
文摘Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
基金Postgraduate Research&Practice Innovation Program of Jiangsu Province“華嚴學與宋代新儒學”.
文摘Piling Canon refers to a woodblock-printed Chinese Buddhist Canon during the late Qing Dynasty.Despite its historical significance,it has received limited attention from the academia,as its discovery took place after the turn of the 21st century.This study explores the background,supervisor,proofreader,engravers,donors,and other factors that contributed to the publication of the Piling Canon.It was supervised by Buddhist monk Qingrong in Changzhou Tianning Monastery from 1908 to 1926,due to the commission of Yang Wenhui.By investigating the historical records in the colophons of Piling Canon,we found that engraving locations are distributed in Hubei,Yangzhou,and Danyang which engravers operated in groups;the majority of donors were found to be individuals and group forms,social fundraising was included as well.It is noteworthy that Sheng Xuanhuai made a significant contribution in terms of funding.Furthermore,the production of the Piling Canon confirms to the commence of Buddhism revival,as Buddhist scriptures in Jiangnan regions were almost destroyed after the Taiping Rebellion.The research shed light on extensive participation of cultural celebrities,diverse donation forms,and excellent engraving,offering a vivid depiction of Buddhist belief and social landscape in Jiangnan region.
文摘The aim was to clarify the environmental driving factors of soil fertility indicators in artificial forests of Guangxi and comprehensively evaluate the soil fertility level.By collecting data on the current status of soil in artificial forests,the spatial distribution of major soil fertility indicators was analyzed,and the distribution map of the fertility index of artificial forests in the entire region and the comprehensive fertility index of artificial forests of different soil types were obtained.Canonical correspondence analysis method was used to analyze soil fertility indicators and environmental factors,and the environmental driving factors of soil fertility indicators for artificial forests of the main soil types in Guangxi were obtained.The results showed that over 90%of the soil fertility index of artificial forests in the entire region was between 0.20 and 0.50.The order of soil fertility index of different soil types of artificial forests from high to low was yellow brown soil>yellow red soil>yellow soil>red soil>limestone soil>latosolic red soil>laterite.In artificial forests of latosolic red soil,the correlation between soil alkaline nitrogen and organic matter,annual average temperature was high,while the correlation between soil available phosphorus and organic matter,pH was high,and the correlation between soil available potassium and environmental factors such as slope,altitude,rainfall,accumulated temperature,and slope aspect was high.In artificial forests of red soil,the correlation between soil alkaline nitrogen and slope,altitude was high,while the correlation between soil available phosphorus and accumulated temperature,rainfall was high,and the correlation between soil available potassium and pH was high.In artificial forests of limestone soil,there was a high correlation between soil alkaline nitrogen and slope,organic matter,a high correlation between soil available phosphorus and accumulated temperature,rainfall,and a high correlation between soil available potassium and pH.
基金supported by the German Research Council(Deutsche Forschungsgemeinschaft,HA3309/3-1/2,HA3309/6-1,HA3309/7-1)。
文摘Skeletal muscles are essential for locomotion,posture,and metabolic regulation.To understand physiological processes,exercise adaptation,and muscle-related disorders,it is critical to understand the molecular pathways that underlie skeletal muscle function.The process of muscle contra ction,orchestrated by a complex interplay of molecular events,is at the core of skeletal muscle function.Muscle contraction is initiated by an action potential and neuromuscular transmission requiring a neuromuscular junction.Within muscle fibers,calcium ions play a critical role in mediating the interaction between actin and myosin filaments that generate force.Regulation of calcium release from the sarcoplasmic reticulum plays a key role in excitation-contraction coupling.The development and growth of skeletal muscle are regulated by a network of molecular pathways collectively known as myogenesis.Myogenic regulators coordinate the diffe rentiation of myoblasts into mature muscle fibers.Signaling pathways regulate muscle protein synthesis and hypertrophy in response to mechanical stimuli and nutrient availability.Seve ral muscle-related diseases,including congenital myasthenic disorders,sarcopenia,muscular dystrophies,and metabolic myopathies,are underpinned by dys regulated molecular pathways in skeletal muscle.Therapeutic interventions aimed at preserving muscle mass and function,enhancing regeneration,and improving metabolic health hold promise by targeting specific molecular pathways.Other molecular signaling pathways in skeletal muscle include the canonical Wnt signaling pathway,a critical regulator of myogenesis,muscle regeneration,and metabolic function,and the Hippo signaling pathway.In recent years,more details have been uncovered about the role of these two pathways during myogenesis and in developing and adult skeletal muscle fibers,and at the neuromuscular junction.In fact,research in the last few years now suggests that these two signaling pathways are interconnected and that they jointly control physiological and pathophysiological processes in muscle fibers.In this review,we will summarize and discuss the data on these two pathways,focusing on their concerted action next to their contribution to skeletal muscle biology.However,an in-depth discussion of the noncanonical Wnt pathway,the fibro/a dipogenic precursors,or the mechanosensory aspects of these pathways is not the focus of this review.
基金supported by Istanbul University Scientific Research Project Department with IRP-51706 Project Number.
文摘Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.
基金supported by the National Natural Science Foundation of China (Nos.61972238,62072294).
文摘Decision implication is a form of decision knowledge represen-tation,which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes.Compared with other forms of decision knowledge representation,decision implication has a stronger knowledge representation capability.Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance.Decision implication canonical basis(DICB)is the most compact set of decision implications,which can efficiently represent all knowledge in the decision context.In order to mine all deci-sion information on decision context under attribute granulating,this paper proposes an updated method of DICB.To this end,the paper reduces the update of DICB to the updates of decision premises after deleting an attribute and after adding granulation attributes of some attributes.Based on this,the paper analyzes the changes of decision premises,examines the properties of decision premises,designs an algorithm for incrementally generating DICB,and verifies its effectiveness through experiments.In real life,by using the updated algorithm of DICB,users may obtain all decision knowledge on decision context after attribute granularization.
基金Supported by National Natural Science Foundation of China,No.82260535National Natural Science Foundation of Guizhou Medical University Hospital Incubation Program,No.gyfynsfc-2022-07.
文摘BACKGROUND Members of the transient receptor potential(TRP)protein family shape oncogenic development,but the specific relevance of TRP-related genes in hepatocellular carcinoma(HCC)has yet to be defined.AIM To investigate the role of TRP genes in HCC,their association with HCC development and treatment was examined.METHODS HCC patient gene expression and clinical data were downloaded from The Cancer Genome Atlas database,and univariate and least absolute shrinkage and selection operator Cox regression models were employed to explore the TRP-related risk spectrum.Based on these analyses,clinically relevant TRP family genes were selected,and the association between the key TRP canonical type 1(TRPC1)gene and HCC patient prognosis was evaluated.RESULTS In total,28 TRP family genes were screened for clinical relevance,with multivariate analyses ultimately revealing three of these genes(TRPC1,TRP cation channel subfamily M member 2,and TRP cation channel subfamily M member 6)to be significantly associated with HCC patient prognosis(P<0.05).These genes were utilized to establish a TRP-related risk model.Patients were separated into low-and high-risk groups based on the expression of these genes,and high-risk patients exhibited a significantly poorer prognosis(P=0.001).Functional analyses highlighted pronounced differences in the immune status of patients in these two groups and associated enriched immune pathways.TRPC1 was identified as a candidate gene in this family worthy of further study,with HCC patients expressing higher TRPC1 levels exhibiting poorer survival outcomes.Consistently,quantitative,immunohistochemistry,and western blot analyses revealed increased TRPC1 expression in HCC.CONCLUSION These three TRP genes help determine HCC patient prognosis,providing insight into tumor immune status and immunological composition.These findings will help design combination therapies including immunotherapeutic and anti-TRP agents.
文摘Globally known about the drivers through which farmers are instigated to uphold and use Hail Canon Technology(HCT)is lacking.Therefore,this article intended to examine the drivers of forecasting the behavioral intention and acceptance behavior of the HCT,249 apple farmers from northwestern Iran were recruited,including adopters(n1=114)and non-adopters(n2=135).The conceptual foundation included demographic theory,resource-based theory,theory of planned behavior,innovation diffusion model,and institutional support model.We also used the system dynamics model(SDM)in the Netlogo to assess the results of the conventional statistical approach(i.e.,the logistic model).Authenticated the fitness of conceptual model with the data,logistic model manifests that the most outstanding determinants of the acceptance of HCT entail age,experience,total land size,income,attitude,compatibility,visibility,relative advantage,and financial support.Using the SDM,it was also shown that the results of the logistic model are confirmed by the SDM.In conclusion,management implications are available for the university extension to eliminate the adoption obstacles and stir up farmers to join in applying HCT,furthermore,researchers would avail themselves of remarks for future research.
基金supported by the National Natural Science Foundation of China(62001195,52071164)the Basic Science(Natural Science)Research Project of Jiangsu Higher Education Institutions(21KJB460030)the Applied Basic Research Programs of Changzhou(CJ20220026)。
文摘Dear Editor,A global and local canonical correlation analysis(GLCCA)based on data-driven is presented for underwater positioning.Underwater positioning technology can help the underwater targets move predetermined destinations for specific tasks[1].Since using different sensor,underwater positioning can be divided into three types:inertial navigation,hydroacoustic positioning and geophysical navigation.
文摘Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11904175,11804169,and 11804165)the Graduate Innovation Project of Jiangsu Province,China(Grant No.KYCX210700)。
文摘Grand canonical Monte Carlo simulation(GCMCs)is utilized for studying hydrogen storage gravimetric density by pha-graphene at different metal densities,temperatures and pressures.It is demonstrated that the optimum adsorbent location for Li atoms is the center of the seven-membered ring of pha-graphene.The binding energy of Li-decorated phagraphene is larger than the cohesive energy of Li atoms,implying that Li can be distributed on the surface of pha-graphene without forming metal clusters.We fitted the force field parameters of Li and C atoms at different positions and performed GCMCs to study the absorption capacity of H_(2).The capacity of hydrogen storage was studied by the differing density of Li decoration.The maximum hydrogen storage capacity of 4Li-decorated pha-graphene was 15.88 wt%at 77 K and100 bar.The enthalpy values of adsorption at the three densities are in the ideal range of 15 kJ·mol^(-1)-25 kJ·mol^(-1).The GCMC results at different pressures and temperatures show that with the increase in Li decorative density,the hydrogen storage gravimetric ratio of pha-graphene decreases but can reach the 2025 US Department of Energy's standard(5.5 wt%).Therefore,pha-graphene is considered to be a potential hydrogen storage material.
