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
Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular st...Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular studies have demonstrated a consistent association between chronic viral infection and B-NHLs.Multiple pathogenic mechanisms have been implicated in lymphomagenesis,both direct and indirect,including chronic antigenic stimulation,direct infection of B cells,and viral protein-mediated oncogenic signaling,It is likely that a combination of several pathogenic conditions is required to eventually lead to the development of lymphoma.The prevalence of B-cell lymphomas among individuals with chronic HCV or HBV infection presents a complex pathogenetic scenario,given the tumor heterogeneity and variable clinical behavior,and poses therapeutic challenges,due to the partial efficacy of current treatment options.The advent of direct-acting antivirals(DAAs)for HCV and high-genetic barrier nucleos(t)ide analogues(NAs)for HBV has improved patient outcomes.In indolent HCV-associated B-NHLs,antiviral therapy with DAAs alone often achieves sustained virologic response and may lead to lymphoma regression.Conversely,aggressive subtypes like diffuse large B-cell lymphomas require combination treatment with immunochemotherapy.In the setting of HBV-associated lymphomas,antiviral prophylaxis with potent NAs(e.g.,entecavir or tenofovir)is essential to prevent HBV reactivation during rituximab-containing chemotherapy regimen.The integration of antiviral and anticancer therapies has been shown to enhance survival outcomes while mitigating hepatic toxicity.A comprehensive understanding of the biological interplay between chronic viral infection and B-cell transformation is critical for optimizing diagnostic and therapeutic strategies.Aim of this viewpoint is to provide evidence that early viral detection and prompt management remain the most effective strategies to improve survival rates and to reduce treatment-related morbidity in these patients.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 Italian Research Council(CNR)“Progetto DSB.AD007.305.001”to Monica Rinaldi。
文摘Hepatitis C virus(HCV)and hepatitis B virus(HBV)infections are increasingly recognized as significant etiological factors in the pathogenesis of B-cell non-Hodgkin’s lymphomas(B-NHLs).Epidemiological and molecular studies have demonstrated a consistent association between chronic viral infection and B-NHLs.Multiple pathogenic mechanisms have been implicated in lymphomagenesis,both direct and indirect,including chronic antigenic stimulation,direct infection of B cells,and viral protein-mediated oncogenic signaling,It is likely that a combination of several pathogenic conditions is required to eventually lead to the development of lymphoma.The prevalence of B-cell lymphomas among individuals with chronic HCV or HBV infection presents a complex pathogenetic scenario,given the tumor heterogeneity and variable clinical behavior,and poses therapeutic challenges,due to the partial efficacy of current treatment options.The advent of direct-acting antivirals(DAAs)for HCV and high-genetic barrier nucleos(t)ide analogues(NAs)for HBV has improved patient outcomes.In indolent HCV-associated B-NHLs,antiviral therapy with DAAs alone often achieves sustained virologic response and may lead to lymphoma regression.Conversely,aggressive subtypes like diffuse large B-cell lymphomas require combination treatment with immunochemotherapy.In the setting of HBV-associated lymphomas,antiviral prophylaxis with potent NAs(e.g.,entecavir or tenofovir)is essential to prevent HBV reactivation during rituximab-containing chemotherapy regimen.The integration of antiviral and anticancer therapies has been shown to enhance survival outcomes while mitigating hepatic toxicity.A comprehensive understanding of the biological interplay between chronic viral infection and B-cell transformation is critical for optimizing diagnostic and therapeutic strategies.Aim of this viewpoint is to provide evidence that early viral detection and prompt management remain the most effective strategies to improve survival rates and to reduce treatment-related morbidity in these patients.
