目的:探究补骨膏对绝经后骨质疏松症(PMOP)大鼠的骨保护作用及其对骨保护素(OPG)-核因子κB受体激活蛋白(RANK)-核因子κB受体激活蛋白配体(RANKL)信号通路的调控作用。方法:将36只大鼠随机分为假手术组(9只)和手术组(27只),手术组采用...目的:探究补骨膏对绝经后骨质疏松症(PMOP)大鼠的骨保护作用及其对骨保护素(OPG)-核因子κB受体激活蛋白(RANK)-核因子κB受体激活蛋白配体(RANKL)信号通路的调控作用。方法:将36只大鼠随机分为假手术组(9只)和手术组(27只),手术组采用双侧卵巢切除建立PMOP大鼠模型。造模成功后,将24只PMOP大鼠随机分为模型组、戊酸雌二醇组、补骨膏低剂量组、补骨膏高剂量组,然后予相应药物灌胃8周。骨密度仪检测股骨近端骨密度;Micro-CT三维重建分析股骨微结构;苏木精-伊红(HE)染色观察股骨组织病理形态;酶联免疫吸附试验(ELISA)法检测血清中骨碱性磷酸酶(BALP)、骨钙素(BGP)、OPG水平,测定盒检测血清中磷、钙水平;蛋白质印迹法(Western blotting)检测股骨组织中OPG、RANK、RANKL蛋白表达水平;RT-qPCR法检测股骨组织肿瘤坏死因子-α(TNF-α)mRNA、干扰素-γ(IFN-γ)mRNA、精氨酸酶-1(Arg-1)mRNA、转化生长因子-β1(TGF-β1)mRNA、基质金属蛋白酶-9(MMP-9)mRNA、OPG mRNA、RANK mRNA、RANKL mRNA表达水平。结果:假手术组大鼠股骨结构连续完整,骨小梁数目较多,形态较厚,结构致密;模型组大鼠股骨近端骨密度明显降低;补骨膏低剂量组、补骨膏高剂量组和戊酸雌二醇组大鼠股骨近端骨小梁数量、骨组织形态结构均得到不同程度改善。模型组大鼠骨密度及血清中钙、BALP、BGP、OPG水平均低于假手术组(P<0.01),血清磷水平高于假手术组(P<0.01);补骨膏低剂量组、补骨膏高剂量组及戊酸雌二醇组大鼠骨密度及血清中钙、BALP、BGP、OPG水平均高于模型组(P<0.05或P<0.01),血清磷低于模型组(P<0.01)。模型组大鼠股骨组织OPG蛋白相对表达量低于假手术组(P<0.01),RANK、RANKL蛋白相对表达量均高于假手术组(P<0.01);补骨膏低剂量组、补骨膏高剂量组及戊酸雌二醇组大鼠股骨组织中OPG蛋白相对表达量高于模型组(P<0.05)或(P<0.01),RANK、RANKL蛋白相对表达量均低于模型组(P<0.01)。模型组大鼠股骨组织TNF-α mRNA、IFN-γ mRNA、MMP-9 m RNA、RANK m RNA、RANKL mRNA对表达量均高于假手术组(P<0.01),Arg-1 m RNA、TGF-β1 mRNA、OPG mRNA对表达量均低于假手术组(P<0.01);补骨膏高剂量组及戊酸雌二醇组大鼠股骨组织TNF-α mRNA、IFN-γ m RNA、MMP-9mRNA、RANK mRNA、RANKL mRNA相对表达量均低于模型组(P<0.01),Arg-1 mRNA、TGF-β1 mRNA、OPG m RNA相对表达量均高于模型组(P<0.01)。结论:补骨膏可能通过调控OPG-RANK-RANKL信号通路,抑制免疫炎症反应,调节骨基质胶原合成与降解,从而维持骨代谢平衡,改善PMOP大鼠骨密度及骨微结构病理损伤。展开更多
On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial ra...This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.展开更多
乳腺癌骨转移(breast cancer bone metastasis,BCBM)是乳腺癌常见并发症,容易引起骨痛、病理性骨折和高钙血症等。骨保护素(osteoprotegerin,OPG)/核因子-κB受体活化因子(receptor activator of nuclear factor-κB,RANK)/RANK配体(RAN...乳腺癌骨转移(breast cancer bone metastasis,BCBM)是乳腺癌常见并发症,容易引起骨痛、病理性骨折和高钙血症等。骨保护素(osteoprotegerin,OPG)/核因子-κB受体活化因子(receptor activator of nuclear factor-κB,RANK)/RANK配体(RANK ligand,RANKL)信号通路在BCBM的发生发展中具有重要作用。研究表明,该信号通路与破骨细胞活化密切相关;BCBM的临床治疗策略包括骨改良药物、分子靶向治疗、免疫疗法等;中药单体及复方制剂能够上调OPG表达抑制破骨细胞活化,并阻断RANKL介导的核因子-κB信号通路激活,从而抑制BCBM病理进程。然而中药的临床应用仍受限于药动学特征的不确定性、质量标准不完善及疗效差异等因素。通过阐述BCBM中OPG/RANK/RANKL信号通路的调控机制,为中药治疗策略的制定提供理论依据。展开更多
In this paper,we first generalize the constant dimension and orbit codes over finite fields to the constant rank and orbit codes over finite chain rings.Then we provide a relationship between constant rank codes over ...In this paper,we first generalize the constant dimension and orbit codes over finite fields to the constant rank and orbit codes over finite chain rings.Then we provide a relationship between constant rank codes over finite chain rings and constant dimension codes over the residue fields.In particular,we prove that an orbit submodule code over a finite chain ring is a constant rank code.Finally,for special finite chain ring F_(q)+γF_(q),we define a Gray mapφfrom(F_(q)+γF_(q))^(n)to F^(2n)_(q),and by using cyclic codes over F_(q)+γF_(q),we obtain a method of constructing an optimum distance constant dimension code over F_(q).展开更多
Steroidal alkaloids are the main active components in many medicinal plants and exhibit diverse biological activities.Axillaridine A(AA)is a newly discovered steroidal alkaloid.However,whether AA could suppress osteoc...Steroidal alkaloids are the main active components in many medicinal plants and exhibit diverse biological activities.Axillaridine A(AA)is a newly discovered steroidal alkaloid.However,whether AA could suppress osteoclastogenesis and alleviate ovariectomy-induced bone loss in mice remains unknown.In vitro,AA significantly suppressed the receptor activator of nuclear factor-κB(NF-κB)ligand(RANKL)-induced osteoclast differentiation via downregulating the expression of osteoclastogenesis-related marker genes,proteins,and transcriptional regulators,including tartrate-resistant acid phosphatase(TRAP),c-Src,matrix metallopeptidase-9(MMP-9),cathepsin K,nuclear factor of activated T cells,cytoplasmic 1(NFATc1),and c-Fos.This was achieved by blocking RANKL-RANK interaction and inhibiting RANKL-mediated RANK signaling pathways,including NF-κB,AKT,and mitogen-activated protein kinases(MAPKs)in osteoclast precursors.In vivo,AA significantly inhibited the ovariectomized(OVX)-induced body weight gain and blood glucose increase in mice.AA did not adversely affect the histomorphologies,weights,and indices of the kidney and liver in OVX mice.AA effectively ameliorated bone loss in OVX mice by inhibiting osteoclastogenesis.AA significantly inhibited the serum levels of tartrate-resistant acid phosphatase 5b(TRACP-5b)and C-telopeptide of type I collagen(CTX-I).AA significantly inhibited the OVX-induced expression of osteoclastogenesis-related marker genes and proteins in the femur.In summary,AA alleviates ovariectomy-induced bone loss in mice by suppressing osteoclastogenesis via inhibition of RANKL-mediated RANK signaling pathways and could be potentially used for the prevention and treatment of osteoclastrelated diseases such as osteoporosis.展开更多
文摘目的:探究补骨膏对绝经后骨质疏松症(PMOP)大鼠的骨保护作用及其对骨保护素(OPG)-核因子κB受体激活蛋白(RANK)-核因子κB受体激活蛋白配体(RANKL)信号通路的调控作用。方法:将36只大鼠随机分为假手术组(9只)和手术组(27只),手术组采用双侧卵巢切除建立PMOP大鼠模型。造模成功后,将24只PMOP大鼠随机分为模型组、戊酸雌二醇组、补骨膏低剂量组、补骨膏高剂量组,然后予相应药物灌胃8周。骨密度仪检测股骨近端骨密度;Micro-CT三维重建分析股骨微结构;苏木精-伊红(HE)染色观察股骨组织病理形态;酶联免疫吸附试验(ELISA)法检测血清中骨碱性磷酸酶(BALP)、骨钙素(BGP)、OPG水平,测定盒检测血清中磷、钙水平;蛋白质印迹法(Western blotting)检测股骨组织中OPG、RANK、RANKL蛋白表达水平;RT-qPCR法检测股骨组织肿瘤坏死因子-α(TNF-α)mRNA、干扰素-γ(IFN-γ)mRNA、精氨酸酶-1(Arg-1)mRNA、转化生长因子-β1(TGF-β1)mRNA、基质金属蛋白酶-9(MMP-9)mRNA、OPG mRNA、RANK mRNA、RANKL mRNA表达水平。结果:假手术组大鼠股骨结构连续完整,骨小梁数目较多,形态较厚,结构致密;模型组大鼠股骨近端骨密度明显降低;补骨膏低剂量组、补骨膏高剂量组和戊酸雌二醇组大鼠股骨近端骨小梁数量、骨组织形态结构均得到不同程度改善。模型组大鼠骨密度及血清中钙、BALP、BGP、OPG水平均低于假手术组(P<0.01),血清磷水平高于假手术组(P<0.01);补骨膏低剂量组、补骨膏高剂量组及戊酸雌二醇组大鼠骨密度及血清中钙、BALP、BGP、OPG水平均高于模型组(P<0.05或P<0.01),血清磷低于模型组(P<0.01)。模型组大鼠股骨组织OPG蛋白相对表达量低于假手术组(P<0.01),RANK、RANKL蛋白相对表达量均高于假手术组(P<0.01);补骨膏低剂量组、补骨膏高剂量组及戊酸雌二醇组大鼠股骨组织中OPG蛋白相对表达量高于模型组(P<0.05)或(P<0.01),RANK、RANKL蛋白相对表达量均低于模型组(P<0.01)。模型组大鼠股骨组织TNF-α mRNA、IFN-γ mRNA、MMP-9 m RNA、RANK m RNA、RANKL mRNA对表达量均高于假手术组(P<0.01),Arg-1 m RNA、TGF-β1 mRNA、OPG mRNA对表达量均低于假手术组(P<0.01);补骨膏高剂量组及戊酸雌二醇组大鼠股骨组织TNF-α mRNA、IFN-γ m RNA、MMP-9mRNA、RANK mRNA、RANKL mRNA相对表达量均低于模型组(P<0.01),Arg-1 mRNA、TGF-β1 mRNA、OPG m RNA相对表达量均高于模型组(P<0.01)。结论:补骨膏可能通过调控OPG-RANK-RANKL信号通路,抑制免疫炎症反应,调节骨基质胶原合成与降解,从而维持骨代谢平衡,改善PMOP大鼠骨密度及骨微结构病理损伤。
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(No.ZCLQN25A0103)。
文摘This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
文摘乳腺癌骨转移(breast cancer bone metastasis,BCBM)是乳腺癌常见并发症,容易引起骨痛、病理性骨折和高钙血症等。骨保护素(osteoprotegerin,OPG)/核因子-κB受体活化因子(receptor activator of nuclear factor-κB,RANK)/RANK配体(RANK ligand,RANKL)信号通路在BCBM的发生发展中具有重要作用。研究表明,该信号通路与破骨细胞活化密切相关;BCBM的临床治疗策略包括骨改良药物、分子靶向治疗、免疫疗法等;中药单体及复方制剂能够上调OPG表达抑制破骨细胞活化,并阻断RANKL介导的核因子-κB信号通路激活,从而抑制BCBM病理进程。然而中药的临床应用仍受限于药动学特征的不确定性、质量标准不完善及疗效差异等因素。通过阐述BCBM中OPG/RANK/RANKL信号通路的调控机制,为中药治疗策略的制定提供理论依据。
基金Supported by Research Funds of Hubei Province(D20144401,Q20174503)。
文摘In this paper,we first generalize the constant dimension and orbit codes over finite fields to the constant rank and orbit codes over finite chain rings.Then we provide a relationship between constant rank codes over finite chain rings and constant dimension codes over the residue fields.In particular,we prove that an orbit submodule code over a finite chain ring is a constant rank code.Finally,for special finite chain ring F_(q)+γF_(q),we define a Gray mapφfrom(F_(q)+γF_(q))^(n)to F^(2n)_(q),and by using cyclic codes over F_(q)+γF_(q),we obtain a method of constructing an optimum distance constant dimension code over F_(q).
基金supported by the grants from the National Natural Science Foundation of China(82404638)the Xingdian Talent Plan of Yunnan Province(XDYC-QNRC-2023-0427 and XDYC-YLXZ2022-0025)the Natural Science Foundation of Yunnan Province(202101BD070001-034,202101BD070001-049,202201AT070267,and 202201AU070183)。
文摘Steroidal alkaloids are the main active components in many medicinal plants and exhibit diverse biological activities.Axillaridine A(AA)is a newly discovered steroidal alkaloid.However,whether AA could suppress osteoclastogenesis and alleviate ovariectomy-induced bone loss in mice remains unknown.In vitro,AA significantly suppressed the receptor activator of nuclear factor-κB(NF-κB)ligand(RANKL)-induced osteoclast differentiation via downregulating the expression of osteoclastogenesis-related marker genes,proteins,and transcriptional regulators,including tartrate-resistant acid phosphatase(TRAP),c-Src,matrix metallopeptidase-9(MMP-9),cathepsin K,nuclear factor of activated T cells,cytoplasmic 1(NFATc1),and c-Fos.This was achieved by blocking RANKL-RANK interaction and inhibiting RANKL-mediated RANK signaling pathways,including NF-κB,AKT,and mitogen-activated protein kinases(MAPKs)in osteoclast precursors.In vivo,AA significantly inhibited the ovariectomized(OVX)-induced body weight gain and blood glucose increase in mice.AA did not adversely affect the histomorphologies,weights,and indices of the kidney and liver in OVX mice.AA effectively ameliorated bone loss in OVX mice by inhibiting osteoclastogenesis.AA significantly inhibited the serum levels of tartrate-resistant acid phosphatase 5b(TRACP-5b)and C-telopeptide of type I collagen(CTX-I).AA significantly inhibited the OVX-induced expression of osteoclastogenesis-related marker genes and proteins in the femur.In summary,AA alleviates ovariectomy-induced bone loss in mice by suppressing osteoclastogenesis via inhibition of RANKL-mediated RANK signaling pathways and could be potentially used for the prevention and treatment of osteoclastrelated diseases such as osteoporosis.
