Food-derived bioactive peptides exhibit good health-promoting effects.In this study,low molecular weight chicken intestine peptide(CIP)below 2 kDa is prepared through trypsin hydrolysis of chicken intestine and ultraf...Food-derived bioactive peptides exhibit good health-promoting effects.In this study,low molecular weight chicken intestine peptide(CIP)below 2 kDa is prepared through trypsin hydrolysis of chicken intestine and ultrafiltration,and the hepatoprotective effects of CIP and underlying mechanisms were investigated.Our findings indicated that CIP alleviated acute liver injury induced by alcohol in mice.Because of iron chelating properties and free radical scavenging activities,CIP inhibited the overload of free iron and the accumulation of lipid peroxides in liver tissues,thereby suppressing alcohol-induced ferroptosis of hepatocytes.In vitro studies demonstrated that CIP suppressed lipid peroxidation and subsequent ferroptosis of HepG2 cells induced by hydrogen peroxide.Mechanistic studies further revealed that CIP promoted the expression and activation of nuclear factor erythroid 2-related factor 2(NRF2),leading to the increased transcription of antioxidant response elements and solute carrier family 7 member 11(SLC7A11).This promoted the synthesis of glutathione and enhanced the enzyme activity of glutathione peroxidase 4(GPX4),facilitating the clearance of lipid peroxides.These findings suggest that CIP holds promise for the prevention and treatment of alcoholic liver disease and other oxidative stress-related liver diseases.展开更多
Hip joint moments during walking are the key foundation for hip exoskeleton assistance control.Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generat...Hip joint moments during walking are the key foundation for hip exoskeleton assistance control.Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation,such as simple sensor requirements and adaptability to variable walking speeds.However,existing joint moment estimation methods still suffer from a lack of personalization,leading to estimation accuracy degradation for new users.To address the challenges,this paper proposes a hip joint moment estimation method based on generalized moment features(GMF).A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder.Utilizing this well-featured representation,a GRU-based neural network is used to predict GMF with joint kinematics data,which can easily be acquired by hip exoskeleton encoders.The proposed estimation method achieves a root mean square error of 0.1180±0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset,improved by 6.5%compared to the model without body parameter fusion,and by 8.3%for the conventional fusion model with body parameter.Furthermore,the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5%metabolic reduction(p<0.01)for users compared to assist-off condition in level-ground walking.展开更多
Inspired by Benjamini et al.(2010) and Windisch(2010),we consider the entropy of the random walk ranges Rn formed by the first n steps of a random walk S on a discrete group.In this setting,we show the existence of hR...Inspired by Benjamini et al.(2010) and Windisch(2010),we consider the entropy of the random walk ranges Rn formed by the first n steps of a random walk S on a discrete group.In this setting,we show the existence of hR:=limn→∞H(Rn)/n called the asymptotic entropy of the ranges.A sample version of the above statement in the sense of Shannon(1948) is also proved.This answers a question raised by Windisch(2010).We also present a systematic characterization of the vanishing asymptotic entropy of the ranges.Particularly,we show that hR=0 if and only if the random walk either is recurrent or escapes to negative infinity without left jump.By introducing the weighted digraphs Γn formed by the underlying random walk,we can characterize the recurrence property of S as the vanishing property of the quantity limn→∞H(Γn)/n,which is an analogue of hR.展开更多
Given n samples(viewed as an n-tuple)of aγ-regular discrete distributionπ,in this article the authors concern with the weighted and unweighted graphs induced by the n samples.They first prove a series of SLLN result...Given n samples(viewed as an n-tuple)of aγ-regular discrete distributionπ,in this article the authors concern with the weighted and unweighted graphs induced by the n samples.They first prove a series of SLLN results(of Dvoretzky-Erdos'type).Then they show that the vertex weights of the graphs under investigation obey asymptotically power law distributions with exponent 1+γThey also give a conjecture that the degrees of unweighted graphs would exhibit asymptotically power law distributions with constant exponent 2.This exponent is obviously independent of the parameterγ∈(0,1),which is a surprise to us at first sight.展开更多
基金supported by National Natural Science Foundation of China(T2341004 and 82174054)Natural Science Foundation of Guangdong(2023B1515040016,2021B1515120023 and 2023LSYS002)+2 种基金Science and Technology Program of Guangzhou(2024A03J090)the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program(2017BT01Y036)the Innovation Team Project of Guangdong Provincial Department of Education(2020KCXTD003)and GDUPS(2019).
文摘Food-derived bioactive peptides exhibit good health-promoting effects.In this study,low molecular weight chicken intestine peptide(CIP)below 2 kDa is prepared through trypsin hydrolysis of chicken intestine and ultrafiltration,and the hepatoprotective effects of CIP and underlying mechanisms were investigated.Our findings indicated that CIP alleviated acute liver injury induced by alcohol in mice.Because of iron chelating properties and free radical scavenging activities,CIP inhibited the overload of free iron and the accumulation of lipid peroxides in liver tissues,thereby suppressing alcohol-induced ferroptosis of hepatocytes.In vitro studies demonstrated that CIP suppressed lipid peroxidation and subsequent ferroptosis of HepG2 cells induced by hydrogen peroxide.Mechanistic studies further revealed that CIP promoted the expression and activation of nuclear factor erythroid 2-related factor 2(NRF2),leading to the increased transcription of antioxidant response elements and solute carrier family 7 member 11(SLC7A11).This promoted the synthesis of glutathione and enhanced the enzyme activity of glutathione peroxidase 4(GPX4),facilitating the clearance of lipid peroxides.These findings suggest that CIP holds promise for the prevention and treatment of alcoholic liver disease and other oxidative stress-related liver diseases.
基金supported by National Key R&D Program of China(2024YFC3082800)National Natural Science Foundation of China(52175272)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2024B1515020008 and 2023B1515130007)Shenzhen Science and Technology Program(KCXFZ20230731093401004,RCYX20231211090345058 and JCYJ20220530114809021).
文摘Hip joint moments during walking are the key foundation for hip exoskeleton assistance control.Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation,such as simple sensor requirements and adaptability to variable walking speeds.However,existing joint moment estimation methods still suffer from a lack of personalization,leading to estimation accuracy degradation for new users.To address the challenges,this paper proposes a hip joint moment estimation method based on generalized moment features(GMF).A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder.Utilizing this well-featured representation,a GRU-based neural network is used to predict GMF with joint kinematics data,which can easily be acquired by hip exoskeleton encoders.The proposed estimation method achieves a root mean square error of 0.1180±0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset,improved by 6.5%compared to the model without body parameter fusion,and by 8.3%for the conventional fusion model with body parameter.Furthermore,the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5%metabolic reduction(p<0.01)for users compared to assist-off condition in level-ground walking.
基金National Natural Science Foundation of China (Grant Nos. 11790273, 11771286, 11531001, 11371317 and 11271077)the Laboratory of Mathematics for Nonlinear Science, Fudan Universitysupported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ18A010007)。
文摘Inspired by Benjamini et al.(2010) and Windisch(2010),we consider the entropy of the random walk ranges Rn formed by the first n steps of a random walk S on a discrete group.In this setting,we show the existence of hR:=limn→∞H(Rn)/n called the asymptotic entropy of the ranges.A sample version of the above statement in the sense of Shannon(1948) is also proved.This answers a question raised by Windisch(2010).We also present a systematic characterization of the vanishing asymptotic entropy of the ranges.Particularly,we show that hR=0 if and only if the random walk either is recurrent or escapes to negative infinity without left jump.By introducing the weighted digraphs Γn formed by the underlying random walk,we can characterize the recurrence property of S as the vanishing property of the quantity limn→∞H(Γn)/n,which is an analogue of hR.
基金This work was supported by the National Natural Science Foundation of China(Nos.11871162,11771286,11271255,11271077,11001173,11790273)the Key Laboratory of Mathematics for Non linear Science,Fudan University.
文摘Given n samples(viewed as an n-tuple)of aγ-regular discrete distributionπ,in this article the authors concern with the weighted and unweighted graphs induced by the n samples.They first prove a series of SLLN results(of Dvoretzky-Erdos'type).Then they show that the vertex weights of the graphs under investigation obey asymptotically power law distributions with exponent 1+γThey also give a conjecture that the degrees of unweighted graphs would exhibit asymptotically power law distributions with constant exponent 2.This exponent is obviously independent of the parameterγ∈(0,1),which is a surprise to us at first sight.
