Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-...The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
For the Sylvester continued fraction expansions of real numbers,FAN et al.(2007)proved that,for almost all real numbers,the nth partial quotient grows exponentially with respect to the product of the first n-1 partial...For the Sylvester continued fraction expansions of real numbers,FAN et al.(2007)proved that,for almost all real numbers,the nth partial quotient grows exponentially with respect to the product of the first n-1 partial quotients.In this paper,we establish the Hausdorff dimension of the exceptional set where the growth rate is a general function.展开更多
Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canol...Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canola.Butisanstar(BUT)and clopyralid(CLO)are widely used for broadleaf weed control in these rotations.However,how residual herbicide activity influences cotton growth and development is not well understood.This study evaluated these residual effects by measuring multiple growth parameters in a greenhouse.Cotton was grown for 40 days in soil incubated for 90 days with herbicide treatments arranged in a factorial design(type:BUT,CLO,and their combination;dose:0,1/2,1,2,and 5×recommended field dose[RFD]).Results Herbicide residues reduced cotton growth in a dose-dependent manner,with greater inhibition at higher doses.The combined BUT+CLO treatment produced the strongest negative effects,followed by CLO and then BUT alone.Compared with controls,seedling emergence declined by 12%–83%,root length by 12%–87%,plant height by 10%–84%,and chlorophyll index by 12%–80%across treatments from 1/2×RFD BUT to 5×RFD BUT+CLO.Root and shoot biomass also decreased significantly.Under the 5×RFD combined treatment,shoot N,P,and K concentrations dropped by 48%,78%,and 70%,respectively,relative to the control.Conclusions Even low levels of residual BUT and CLO impair cotton growth.To mitigate these effects,it should avoid planting cotton on recently treated soils,leave sufficient intervals between herbicide application and cotton planting,and apply soil amendments to boost microbial degradation.These measures are essential for sustaining soil health and cotton productivity.展开更多
Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applicati...Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
This study investigates the corrosion-assisted fatigue crack growth rate(FCGR)of 16 mm thick AA 7075-T651 friction stir welded(FSW)joints.Compact tension(CT)specimens were extracted from both the base material and FSW...This study investigates the corrosion-assisted fatigue crack growth rate(FCGR)of 16 mm thick AA 7075-T651 friction stir welded(FSW)joints.Compact tension(CT)specimens were extracted from both the base material and FSW joints to evaluate FCGR under varying corrosion exposure durations(0,7,30,60,and 90 days)at a constant stress ratio of 0.5.Microstructural analysis of the welds was conducted using optical and transmission electron microscopy(TEM).Results indicate that the critical stress intensity factor range(ΔK_(cr))of FSW joints is lower than that of the base material,primarily due to precipitate dissolution in the weld zone during the FSW process,as confirmed by TEM analysis.The fatigue life of FSW joints was significantly lower than that of the base material,but with prolonged exposure to seawater corrosion,the gap in fatigue life narrowed.Specimens exposed to seawater for more than 60days exhibited minimal differences in fatigue life between the base material and the FSW joints.This was attributed to the higher corrosion rate of the base material compared to the weld nugget,resulting in the formation of deeper pits that facilitated crack initiation and accelerated fatigue failure.The findings conclude that extended corrosion exposure leads to similar fatigue life and crack growth behaviour in both the base material and FSW joints.SEM and EDX analysis of AA7075-T651 revealed corrosion pits and rust products in initiation zones,ductile striations in growth regions,and secondary cracks with micro voids in fracture zones.FSW joints exhibited ultra-fine grains,smooth ductile fracture in initiation and growth regions,and brittle fracture in the fracture zones under both corroded and uncorroded conditions.展开更多
In recent years,growth hormone and insulin-like growth factors have become key regulators of bone metabolism and remodeling,crucial for maintaining healthy bone mass throughout life.Studies have shown that adult growt...In recent years,growth hormone and insulin-like growth factors have become key regulators of bone metabolism and remodeling,crucial for maintaining healthy bone mass throughout life.Studies have shown that adult growth hormone deficiency leads to alterations in bone remodeling,significantly affecting bone microarchitecture and increasing fracture risk.Although recombinant human growth hormone replacement therapy can mitigate these adverse effects,improving bone density,and reduce fracture risk,its effectiveness in treating osteoporosis,especially in adults with established growth hormone deficiency,seems limited.Bisphosphonates inhibit bone resorption by targeting farnesyl pyrophosphate synthase in osteoclasts,and clinical trials have confirmed their efficacy in improving osteoporosis.Therefore,for adult growth hormone deficiency patients with osteoporosis,the use of bisphosphonates alongside growth hormone replacement therapy is recommended.展开更多
OBJECTIVE Basic fibroblast growth factor(b FGF)and platelet-derived growth factor(PDGF)produced by hepatocellular carcinoma(HCC)cells are responsible for the cell growth.Accumulating evidence shows that insulin-like g...OBJECTIVE Basic fibroblast growth factor(b FGF)and platelet-derived growth factor(PDGF)produced by hepatocellular carcinoma(HCC)cells are responsible for the cell growth.Accumulating evidence shows that insulin-like growth factor-binding protein-3(IGFBP-3)suppresses HCC cell proliferation in both IGF-dependent and independent manners.The present study is to investigate whether treatment with exogenous IGFBP-3 inhibits bF GF and PDGF production and the cell proliferation of HCC cells.METHODS Cell Counting Kit 8 assay were designed to detect HCC cell proliferation,transcription factor early growth response-1(EGR1)involving in IGFBP-3 regulation of b FGF and PDGF were detected by RT-PCR and Western blot assays.Western blot assay was adopted to detect the IGFBP-3 regulating insulin-like growth factor 1 receptor(IGF-1R)signaling pathway.RESULTS The present study demonstrates that IGFBP-3 suppressed IGF-1-induced b FGF and PDGF expression while it does not affect their expression in the absence of IGF-1.To delineate the underlying mechanism,Western-blot and RT-PCR assays confirmed that the transcription factor early growth response protein 1(EGR1)is involved in IGFBP-3 regulation of b FGF and PDGF.IGFBP-3 inhibition of type 1 insulin-like growth factor receptor(IGF1R),ERK and AKT activation is IGF-1-dependent.Furthermore,transient transfection with constitutively activated AKT or MEK partially blocks the IGFBP-3 inhibition of EGR1,b FGF and PDGF expression.CONCLUSION In conclusion,these findings suggest that IGFBP-3suppresses transcription of EGR1 and its target genes b FGF and PDGF through inhibiting IGF-1-dependent ERK and AKT activation.It demonstrates the importance of IGFBP-3 in the regulation of HCC cell proliferation,suggesting that IGFBP-3 could be a target for the treatment of HCC.展开更多
The increasing of the growth rate of the crystals from aqueous solutions(simultaneously keeping a good quality of the crystals)remains the important problem.A comparison of fast grown and low grown KDP crystals shows,...The increasing of the growth rate of the crystals from aqueous solutions(simultaneously keeping a good quality of the crystals)remains the important problem.A comparison of fast grown and low grown KDP crystals shows,that some properties of the former are often better than low grown materials.展开更多
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金Project(2018YFC1900403) supported by the National Key Research and Development Program of ChinaProject(CX20210197) supported by the Postgraduate Scientific Research Innovation Project of Hunan Province,China+1 种基金Project(202206370103) supported by the China Scholarship CouncilProject(2021zzts0115) supported by the Fundamental Research Funds for the Central Universities,China。
文摘The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金Supported by Projects from Chongqing Municipal Science and Technology Commission(CSTB2022NSCQ-MSX0445)。
文摘For the Sylvester continued fraction expansions of real numbers,FAN et al.(2007)proved that,for almost all real numbers,the nth partial quotient grows exponentially with respect to the product of the first n-1 partial quotients.In this paper,we establish the Hausdorff dimension of the exceptional set where the growth rate is a general function.
文摘Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canola.Butisanstar(BUT)and clopyralid(CLO)are widely used for broadleaf weed control in these rotations.However,how residual herbicide activity influences cotton growth and development is not well understood.This study evaluated these residual effects by measuring multiple growth parameters in a greenhouse.Cotton was grown for 40 days in soil incubated for 90 days with herbicide treatments arranged in a factorial design(type:BUT,CLO,and their combination;dose:0,1/2,1,2,and 5×recommended field dose[RFD]).Results Herbicide residues reduced cotton growth in a dose-dependent manner,with greater inhibition at higher doses.The combined BUT+CLO treatment produced the strongest negative effects,followed by CLO and then BUT alone.Compared with controls,seedling emergence declined by 12%–83%,root length by 12%–87%,plant height by 10%–84%,and chlorophyll index by 12%–80%across treatments from 1/2×RFD BUT to 5×RFD BUT+CLO.Root and shoot biomass also decreased significantly.Under the 5×RFD combined treatment,shoot N,P,and K concentrations dropped by 48%,78%,and 70%,respectively,relative to the control.Conclusions Even low levels of residual BUT and CLO impair cotton growth.To mitigate these effects,it should avoid planting cotton on recently treated soils,leave sufficient intervals between herbicide application and cotton planting,and apply soil amendments to boost microbial degradation.These measures are essential for sustaining soil health and cotton productivity.
文摘Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
文摘This study investigates the corrosion-assisted fatigue crack growth rate(FCGR)of 16 mm thick AA 7075-T651 friction stir welded(FSW)joints.Compact tension(CT)specimens were extracted from both the base material and FSW joints to evaluate FCGR under varying corrosion exposure durations(0,7,30,60,and 90 days)at a constant stress ratio of 0.5.Microstructural analysis of the welds was conducted using optical and transmission electron microscopy(TEM).Results indicate that the critical stress intensity factor range(ΔK_(cr))of FSW joints is lower than that of the base material,primarily due to precipitate dissolution in the weld zone during the FSW process,as confirmed by TEM analysis.The fatigue life of FSW joints was significantly lower than that of the base material,but with prolonged exposure to seawater corrosion,the gap in fatigue life narrowed.Specimens exposed to seawater for more than 60days exhibited minimal differences in fatigue life between the base material and the FSW joints.This was attributed to the higher corrosion rate of the base material compared to the weld nugget,resulting in the formation of deeper pits that facilitated crack initiation and accelerated fatigue failure.The findings conclude that extended corrosion exposure leads to similar fatigue life and crack growth behaviour in both the base material and FSW joints.SEM and EDX analysis of AA7075-T651 revealed corrosion pits and rust products in initiation zones,ductile striations in growth regions,and secondary cracks with micro voids in fracture zones.FSW joints exhibited ultra-fine grains,smooth ductile fracture in initiation and growth regions,and brittle fracture in the fracture zones under both corroded and uncorroded conditions.
基金This work was supported by the Special Project of Performance Incentive and Guidance for Scientific Research Institutions of Chongqing,China (jxyn2022-5)。
文摘In recent years,growth hormone and insulin-like growth factors have become key regulators of bone metabolism and remodeling,crucial for maintaining healthy bone mass throughout life.Studies have shown that adult growth hormone deficiency leads to alterations in bone remodeling,significantly affecting bone microarchitecture and increasing fracture risk.Although recombinant human growth hormone replacement therapy can mitigate these adverse effects,improving bone density,and reduce fracture risk,its effectiveness in treating osteoporosis,especially in adults with established growth hormone deficiency,seems limited.Bisphosphonates inhibit bone resorption by targeting farnesyl pyrophosphate synthase in osteoclasts,and clinical trials have confirmed their efficacy in improving osteoporosis.Therefore,for adult growth hormone deficiency patients with osteoporosis,the use of bisphosphonates alongside growth hormone replacement therapy is recommended.
基金supported by National Natural Science Foundation of China(81502123 and81330081)Natural Science Foundation of Anhui Province(1308085QH130)Anhui Province Nature Science Foundation in University(KJ2014A119)
文摘OBJECTIVE Basic fibroblast growth factor(b FGF)and platelet-derived growth factor(PDGF)produced by hepatocellular carcinoma(HCC)cells are responsible for the cell growth.Accumulating evidence shows that insulin-like growth factor-binding protein-3(IGFBP-3)suppresses HCC cell proliferation in both IGF-dependent and independent manners.The present study is to investigate whether treatment with exogenous IGFBP-3 inhibits bF GF and PDGF production and the cell proliferation of HCC cells.METHODS Cell Counting Kit 8 assay were designed to detect HCC cell proliferation,transcription factor early growth response-1(EGR1)involving in IGFBP-3 regulation of b FGF and PDGF were detected by RT-PCR and Western blot assays.Western blot assay was adopted to detect the IGFBP-3 regulating insulin-like growth factor 1 receptor(IGF-1R)signaling pathway.RESULTS The present study demonstrates that IGFBP-3 suppressed IGF-1-induced b FGF and PDGF expression while it does not affect their expression in the absence of IGF-1.To delineate the underlying mechanism,Western-blot and RT-PCR assays confirmed that the transcription factor early growth response protein 1(EGR1)is involved in IGFBP-3 regulation of b FGF and PDGF.IGFBP-3 inhibition of type 1 insulin-like growth factor receptor(IGF1R),ERK and AKT activation is IGF-1-dependent.Furthermore,transient transfection with constitutively activated AKT or MEK partially blocks the IGFBP-3 inhibition of EGR1,b FGF and PDGF expression.CONCLUSION In conclusion,these findings suggest that IGFBP-3suppresses transcription of EGR1 and its target genes b FGF and PDGF through inhibiting IGF-1-dependent ERK and AKT activation.It demonstrates the importance of IGFBP-3 in the regulation of HCC cell proliferation,suggesting that IGFBP-3 could be a target for the treatment of HCC.
文摘The increasing of the growth rate of the crystals from aqueous solutions(simultaneously keeping a good quality of the crystals)remains the important problem.A comparison of fast grown and low grown KDP crystals shows,that some properties of the former are often better than low grown materials.
