Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this...Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.展开更多
Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders...Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders,and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.However,the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined.Here,we applied a disease similarity approach to identify unknown molecular targets of Alzheimer’s disease by using transcriptomic data from congenital diseases known to increase Alzheimer’s disease risk,namely Down syndrome,Niemann-Pick type C disease,and mucopolysaccharidoses I.We uncovered common pathways,hub genes,and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of Alzheimer’s disease pathology,many of which have never been associated with Alzheimer’s disease.We then investigated common molecular alterations in brain samples from a Niemann-Pick type C disease mouse model by juxtaposing them with brain samples of both human and mouse models of Alzheimer’s disease.Detailed phenotypic,molecular,chronological,and biological aging analyses revealed that the Npc1tm(I1061T)Dso mouse model can serve as a potential short-lived in vivo model for brain aging and Alzheimer’s disease research.This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on Alzheimer’s disease research while highlighting shortcomings and lack of correlation in diverse in vitro models.Considering the lack of an Alzheimer’s disease mouse model that recapitulates the physiological hallmarks of brain aging,the short-lived Npc1^(tm(I1061T)Dso) mouse model can further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of Alzheimer’s disease from a perspective of accelerated brain aging.展开更多
Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol chan...Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol changes that occur in the brain during Alzheimer’s disease remains unclear.In this study,we compared brain tissues extracted from 32-week-old male wild-type mice and 5×FAD transgenic Alzheimer’s disease model mice,which carry mutations in the amyloid precursor protein(APP)and presenilin 1(PS1)genes.Using untargeted lipidomics and sterolomics techniques,we investigated the metabolic profiles of lipids,with a focus on sterols specifically,in three brain regions:cerebellum,hippocampus,and olfactory bulb.Our results revealed significant alterations in various lipids,particularly in the hippocampus and olfactory bulb,suggesting changes in energy levels in these regions.Further pathway analysis indicated notable disruptions in key metabolic processes,particularly those related to fatty acids and cell membrane components.Additionally,we observed decreased expression of 15 genes involved in lipid and sterol regulation.Collectively,these findings provide new insights into how imbalances in lipid and sterol metabolism may contribute to the progression of Alzheimer’s disease,highlighting potential metabolic pathways involved in the development of this debilitating disease.展开更多
Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patien...Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.展开更多
Background:In recent decades,the global incidence of dengue fever has been stead-ily increasing,with continuous geographical expansion.Researchers have successfully modeled most clinical symptoms of human dengue fever...Background:In recent decades,the global incidence of dengue fever has been stead-ily increasing,with continuous geographical expansion.Researchers have successfully modeled most clinical symptoms of human dengue fever using interferon type I(IFN-I)or combined IFN-I/II receptor knockout mice infected with dengue virus(DENV).However,this model requires further optimization to better support related studies.Methods:This study aimed to establish a stable dengue infection model by evaluating the effects of different genetic backgrounds and injection routes on DENV infection in interferon receptor knockout mice.We first infected various strains of interferon receptor-deficient mice with DENV and compared their susceptibility based on clini-cal symptoms,viremia levels,organ indices,histopathological findings,and vascular leakage markers.Subsequently,we selected the most susceptible strain to further investigate the impact of different injection methods on infection outcomes.Results:We found that BALB/c background mice with type 1 interferon recep-tor knockout(IFNAR)had the most obvious symptoms.Subsequently,we selected IFNAR−/−BALB/c mice to further explore the effects of different injection methods on dengue virus infection.The results showed that the intraperitoneal injection group had the most severe clinical symptoms,the longest duration of viremia,and the most obvious degree of organ damage.Conclusion:Through systematic screening and optimization,we established a robust animal model of dengue virus infection via intraperitoneal injection in IFNAR−/−BALB/c mice.This model offers a valuable tool for future dengue research.展开更多
The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variabl...The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variables as well as their interactions remain unclear,especially in areas undergoing long-term forest restoration.In this study,Ca and Mg concentrations in the forest floor and topsoil from 239 forest plots across the Loess Plateau were measured,and the effects of forest types,climate,soil properties,stand characteristics and nitrogen deposition were explored.The results showed significantly higher Ca concentrations in the forest floor(20.68±8.04 mg/g)than in the topsoil(13.28±12.83 mg/g),whereas Mg exhibited the inverse pattern(3.64±1.09 and 10.11±2.51 mg/g,respectively).The effect of forest types was only significant on forest floor Ca,and Ca concentrations were higher in broadleaf and mixed forests than in coniferous forests.Overall,Ca and Mg concentrations in forest floor and topsoil increased with latitudes while decreased with elevations,and the significance of the trends varied among forest types.Forest floor Ca and Mg were mainly influenced by environmental variables aboveground,i.e.,basal area(BA)and mean annual precipitation(MAP),respectively;topsoil Ca and Mg were more affected by soil properties(soil C/N and pH,respectively).Those suggested a depletion of Ca belowground was associated with forest growth and enriched soil nitrogen,and the leaching of mobile Mg was correlated with rainfall and soil acidification.Besides,the impact of environmental variables on Ca-Mg balance(Ca/Mg ratio)belowground was primarily through the regulation of Ca.Elucidating the influence of environmental variables will improve our ability to predict future changes in base cations and thus forest soil health in the greening vegetated Loess Plateau.展开更多
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Tr...Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Traditional approaches have focused on symptom management,but breakthroughs in bioinformatics and high-thro ughput drug screening are offering new pathways to potential therapies.This review highlights our recent effo rts to identify novel drug candidates for Alzheimer's disease by leve raging computational methods and la rge-scale biological datasets.Our work introduces two key innovations in Alzheimer's disease research:addressing sex-specific diffe rences and leve raging drug repurposing for accelerated treatment discove ry.By combining sex-stratified preclinical data with machine learning and in vivo validation,we improve translational relevance and support precision medicine.Using the TgF344-AD rat model,which mimics human Alzheimer's disease spatial memory deficits and pathology,we explored the efficacy of various US Food and Drug Administrationapproved and investigational drugs.These included ibudilast,timapiprant,RG2833,diazoxide/dibenzoylmethane(combined),and BT-11,which targeted key Alzheimer's disease-related molecular pathways such as amyloid-beta plaques,Ta u tangles,and neuroinflammation.These drugs,at various stages of development,offer hope for not only managing symptoms but also addressing the underlying mechanisms of Alzheimer's disease.This review underscores the need for a multifaceted approach to Alzheimer's disease treatment,combining symptom relief with disease modification.展开更多
基金supported by National Science and Technology Council(NSTC)Taiwan,Grant No.NSTC 113-2221-E-167-023.
文摘Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.
基金supported by the NIA/NIH(1K01AG060040).Studies performed by JN were funded by the NICHD/NIH(5R00HD096117)Microscopy Core Facility supported,in part,with funding from NIH-NCI Cancer Center Support Grant P30 CA016059.
文摘Alzheimer’s disease is initially thought to be caused by age-associated accumulation of plaques,in recent years,research has increasingly associated Alzheimer’s disease with lysosomal storage and metabolic disorders,and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.However,the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined.Here,we applied a disease similarity approach to identify unknown molecular targets of Alzheimer’s disease by using transcriptomic data from congenital diseases known to increase Alzheimer’s disease risk,namely Down syndrome,Niemann-Pick type C disease,and mucopolysaccharidoses I.We uncovered common pathways,hub genes,and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of Alzheimer’s disease pathology,many of which have never been associated with Alzheimer’s disease.We then investigated common molecular alterations in brain samples from a Niemann-Pick type C disease mouse model by juxtaposing them with brain samples of both human and mouse models of Alzheimer’s disease.Detailed phenotypic,molecular,chronological,and biological aging analyses revealed that the Npc1tm(I1061T)Dso mouse model can serve as a potential short-lived in vivo model for brain aging and Alzheimer’s disease research.This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on Alzheimer’s disease research while highlighting shortcomings and lack of correlation in diverse in vitro models.Considering the lack of an Alzheimer’s disease mouse model that recapitulates the physiological hallmarks of brain aging,the short-lived Npc1^(tm(I1061T)Dso) mouse model can further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of Alzheimer’s disease from a perspective of accelerated brain aging.
基金supported by the National Natural Science Foundation of China,Nos.82200784,32271311Qizhen Foundation,No.226‐2023‐00008(all to LH).
文摘Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol changes that occur in the brain during Alzheimer’s disease remains unclear.In this study,we compared brain tissues extracted from 32-week-old male wild-type mice and 5×FAD transgenic Alzheimer’s disease model mice,which carry mutations in the amyloid precursor protein(APP)and presenilin 1(PS1)genes.Using untargeted lipidomics and sterolomics techniques,we investigated the metabolic profiles of lipids,with a focus on sterols specifically,in three brain regions:cerebellum,hippocampus,and olfactory bulb.Our results revealed significant alterations in various lipids,particularly in the hippocampus and olfactory bulb,suggesting changes in energy levels in these regions.Further pathway analysis indicated notable disruptions in key metabolic processes,particularly those related to fatty acids and cell membrane components.Additionally,we observed decreased expression of 15 genes involved in lipid and sterol regulation.Collectively,these findings provide new insights into how imbalances in lipid and sterol metabolism may contribute to the progression of Alzheimer’s disease,highlighting potential metabolic pathways involved in the development of this debilitating disease.
基金supported by the Canadian Institutes of Health Research(DFD-181599)the National Institutes of Health(T32AG058527)to RJB and R0190106435 to VM.
文摘Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.
基金Guangdong Province Medical Research Fund Project,Grant/Award Number:B2024112The Scientific Research Special Project of the Joint Construction Project of High-level Hospitals between Guangzhou University of Chinese Medicine and the Scientific Research Fund Project,Grant/Award Number:GZYZS2024G09+2 种基金Special Project of the Research Platform of Guangdong Provincial Department of Traditional Chinese Medicine,Grant/Award Number:20254040the Project of the Incubation Program for the Science and Technology Development of Chinese Medicine Guangdong Laboratory/Hengqin Laboratory,Grant/Award Number:HQL2024PZ043Guangdong Province Natural Science Foundation-Guangzhou-South China Joint Youth Fund Project,Grant/Award Number:2023A1515110849。
文摘Background:In recent decades,the global incidence of dengue fever has been stead-ily increasing,with continuous geographical expansion.Researchers have successfully modeled most clinical symptoms of human dengue fever using interferon type I(IFN-I)or combined IFN-I/II receptor knockout mice infected with dengue virus(DENV).However,this model requires further optimization to better support related studies.Methods:This study aimed to establish a stable dengue infection model by evaluating the effects of different genetic backgrounds and injection routes on DENV infection in interferon receptor knockout mice.We first infected various strains of interferon receptor-deficient mice with DENV and compared their susceptibility based on clini-cal symptoms,viremia levels,organ indices,histopathological findings,and vascular leakage markers.Subsequently,we selected the most susceptible strain to further investigate the impact of different injection methods on infection outcomes.Results:We found that BALB/c background mice with type 1 interferon recep-tor knockout(IFNAR)had the most obvious symptoms.Subsequently,we selected IFNAR−/−BALB/c mice to further explore the effects of different injection methods on dengue virus infection.The results showed that the intraperitoneal injection group had the most severe clinical symptoms,the longest duration of viremia,and the most obvious degree of organ damage.Conclusion:Through systematic screening and optimization,we established a robust animal model of dengue virus infection via intraperitoneal injection in IFNAR−/−BALB/c mice.This model offers a valuable tool for future dengue research.
基金supported by the National Natural Science Foundation of China(42401054)Natural Science Foundation of Hebei Province(D2024205019)Science and Technology Project of Hebei Education Department(BJ2025014).
文摘The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variables as well as their interactions remain unclear,especially in areas undergoing long-term forest restoration.In this study,Ca and Mg concentrations in the forest floor and topsoil from 239 forest plots across the Loess Plateau were measured,and the effects of forest types,climate,soil properties,stand characteristics and nitrogen deposition were explored.The results showed significantly higher Ca concentrations in the forest floor(20.68±8.04 mg/g)than in the topsoil(13.28±12.83 mg/g),whereas Mg exhibited the inverse pattern(3.64±1.09 and 10.11±2.51 mg/g,respectively).The effect of forest types was only significant on forest floor Ca,and Ca concentrations were higher in broadleaf and mixed forests than in coniferous forests.Overall,Ca and Mg concentrations in forest floor and topsoil increased with latitudes while decreased with elevations,and the significance of the trends varied among forest types.Forest floor Ca and Mg were mainly influenced by environmental variables aboveground,i.e.,basal area(BA)and mean annual precipitation(MAP),respectively;topsoil Ca and Mg were more affected by soil properties(soil C/N and pH,respectively).Those suggested a depletion of Ca belowground was associated with forest growth and enriched soil nitrogen,and the leaching of mobile Mg was correlated with rainfall and soil acidification.Besides,the impact of environmental variables on Ca-Mg balance(Ca/Mg ratio)belowground was primarily through the regulation of Ca.Elucidating the influence of environmental variables will improve our ability to predict future changes in base cations and thus forest soil health in the greening vegetated Loess Plateau.
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金National Institutes of Health,No.R01AG057555(to PI,L.Xie,co-l,MEFP,PAS,PR)。
文摘Alzheimer s disease is a neurodegenerative disorder that leads to progressive memory loss,cognitive decline,and behavioral changes.Des pite ongoing research,its exa ct causes and effective treatments remain elusive.Traditional approaches have focused on symptom management,but breakthroughs in bioinformatics and high-thro ughput drug screening are offering new pathways to potential therapies.This review highlights our recent effo rts to identify novel drug candidates for Alzheimer's disease by leve raging computational methods and la rge-scale biological datasets.Our work introduces two key innovations in Alzheimer's disease research:addressing sex-specific diffe rences and leve raging drug repurposing for accelerated treatment discove ry.By combining sex-stratified preclinical data with machine learning and in vivo validation,we improve translational relevance and support precision medicine.Using the TgF344-AD rat model,which mimics human Alzheimer's disease spatial memory deficits and pathology,we explored the efficacy of various US Food and Drug Administrationapproved and investigational drugs.These included ibudilast,timapiprant,RG2833,diazoxide/dibenzoylmethane(combined),and BT-11,which targeted key Alzheimer's disease-related molecular pathways such as amyloid-beta plaques,Ta u tangles,and neuroinflammation.These drugs,at various stages of development,offer hope for not only managing symptoms but also addressing the underlying mechanisms of Alzheimer's disease.This review underscores the need for a multifaceted approach to Alzheimer's disease treatment,combining symptom relief with disease modification.
