Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual reality.This method becomes a trendsetter in bringing the metaverse into vivo life.It h...Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual reality.This method becomes a trendsetter in bringing the metaverse into vivo life.It has initially captured the attention of macroscopic biology,as demonstrated by computed tomography and magnetic resonance imaging,which provide a 3D field of view for diagnostic biological images.Meanwhile,it has also opened up new research opportunities in microscopic imaging,such as achieving clearer de novo protein structure reconstructions.Introducing this method to the field of biology is particularly significant,as it is refining the approach to studying biological images.However,many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D perspectives.This article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine,highlighting the broad prospects of neural fields in the future biological metaverse.We stand at the threshold of an exciting new era,where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.展开更多
The molecular clock model is fundamental for inferring species divergence times from molecular sequences.However,its direct application may introduce significant biases due to sequencing errors,recombination events,an...The molecular clock model is fundamental for inferring species divergence times from molecular sequences.However,its direct application may introduce significant biases due to sequencing errors,recombination events,and inaccurately labeled sampling times.Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences.Furthermore,while not all branches of a phylogenetic tree may exhibit a clear temporal signal,specific branches may still adhere to the assumptions,with varying evolutionary rates.Supporting a relaxed molecular clock model better aligns with the complexities of evolution.The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals.Despite its utility,there remains a lack of corresponding software implementations for broader applications.To address this gap,we present shinyTempSignal,an interactive web application implemented with the shiny framework,available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal.This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models.By extending the root-to-tip regression method to diverse signals,shinyTempSignal helps in the detection of evolving features or traits,thereby laying the foundation for deeper insights and subsequent analyses.展开更多
文摘Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual reality.This method becomes a trendsetter in bringing the metaverse into vivo life.It has initially captured the attention of macroscopic biology,as demonstrated by computed tomography and magnetic resonance imaging,which provide a 3D field of view for diagnostic biological images.Meanwhile,it has also opened up new research opportunities in microscopic imaging,such as achieving clearer de novo protein structure reconstructions.Introducing this method to the field of biology is particularly significant,as it is refining the approach to studying biological images.However,many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D perspectives.This article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine,highlighting the broad prospects of neural fields in the future biological metaverse.We stand at the threshold of an exciting new era,where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.
基金supported by the National Natural Science Foundation of China(32270677).
文摘The molecular clock model is fundamental for inferring species divergence times from molecular sequences.However,its direct application may introduce significant biases due to sequencing errors,recombination events,and inaccurately labeled sampling times.Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences.Furthermore,while not all branches of a phylogenetic tree may exhibit a clear temporal signal,specific branches may still adhere to the assumptions,with varying evolutionary rates.Supporting a relaxed molecular clock model better aligns with the complexities of evolution.The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals.Despite its utility,there remains a lack of corresponding software implementations for broader applications.To address this gap,we present shinyTempSignal,an interactive web application implemented with the shiny framework,available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal.This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models.By extending the root-to-tip regression method to diverse signals,shinyTempSignal helps in the detection of evolving features or traits,thereby laying the foundation for deeper insights and subsequent analyses.
