The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treat...The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.展开更多
In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for develo...In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.展开更多
The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience.Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxi...The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience.Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing:brain image,atlas,and stereotaxis.We also refine four technical indices for evaluating the construction of atlases:the quality of staining and labeling,the granularity of delineation,spatial resolution,and the precision of spatial location and orientation.Additionally,we discuss state-of-the-art technologies and their trends in the fields of image acquisition,stereotaxic coordinate construction,image processing,anatomical structure recognition,and publishing:the procedures of brain atlas illustration.We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas,which will greatly benefit the development of neuroscience.展开更多
The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolut...The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary research.However,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been made.In this study,we created a new whole-brain map,the Macaque Brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data.The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections.The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images.As a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure.The resulting resource includes:(1)the thoroughly delineated Macaque Brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(Brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single macaque.MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function.Therefore,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.展开更多
文摘The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.
基金supported by the National Natural Science Foundation of China(31530084,32000558,32000483,and31800504)the Programme of Introducing Talents of Discipline to Universities(111 project,B13007)the China Postdoctoral Science Foundation Grant(2019M660494)。
文摘In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.
基金supported by the National Natural Science Foundation of China(61721092,81827901,61890950,and 61890951)。
文摘The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience.Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing:brain image,atlas,and stereotaxis.We also refine four technical indices for evaluating the construction of atlases:the quality of staining and labeling,the granularity of delineation,spatial resolution,and the precision of spatial location and orientation.Additionally,we discuss state-of-the-art technologies and their trends in the fields of image acquisition,stereotaxic coordinate construction,image processing,anatomical structure recognition,and publishing:the procedures of brain atlas illustration.We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas,which will greatly benefit the development of neuroscience.
基金partially supported by the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)the National Natural Science Foundation of China(62327805,82151307,82072099,82202253)。
文摘The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary research.However,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been made.In this study,we created a new whole-brain map,the Macaque Brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data.The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections.The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images.As a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure.The resulting resource includes:(1)the thoroughly delineated Macaque Brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(Brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single macaque.MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function.Therefore,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
