Measurement of optical properties of skin is an expanding and growing field of research.Recent studies have shown that the biological tissue,especially skin,changes the polarization state of the incident light.Using t...Measurement of optical properties of skin is an expanding and growing field of research.Recent studies have shown that the biological tissue,especially skin,changes the polarization state of the incident light.Using this property will enable the study of abnormalities and diseases that alter not only the light intensity but also its polarization state.In this paper we report an experimental study for measuring changes of polarization state of the light scattered from a phantom similar to a sample model of scattering skin.Using the notation of Stokes vector for the polarized light and Mueller matrix for the sample with its polarization properties,we have shown that some elements of the matrix were particularly sensitive to the changes of the polarization-altering physical properties of the scatterers within the phantom.展开更多
Post-translational modifications(PTMs)have key roles in extending the functional diversity of proteins and,as a result,regulating diverse cellular processes in prokaryotic and eukaryotic organisms.Phosphorylation modi...Post-translational modifications(PTMs)have key roles in extending the functional diversity of proteins and,as a result,regulating diverse cellular processes in prokaryotic and eukaryotic organisms.Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes.Disorders in the phosphorylation process lead to multiple diseases,including neurological disorders and cancers.The purpose of this review is to organize this body of knowledge associated with phosphorylation site(p-site)prediction to facilitate future research in this field.At first,we comprehensively review all related databases and introduce all steps regarding dataset creation,data preprocessing,and method evaluation in p-site prediction.Next,we investigate p-site prediction methods,which are divided into two computational groups:algorithmic and machine learning(ML).Additionally,it is shown that there are basically two main approaches for p-site prediction by ML:conventional and end-to-end deep learning methods,both of which are given an overview.Moreover,this review introduces the most important feature extraction techniques,which have mostly been used in p-site prediction.Finally,we create three test sets from new proteins related to the released version of the database of protein post-translational modifications(dbPTM)in 2022 based on general and human species.Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release,distinct from those in the dbPTM 2019 release,reveals their limitations.In other words,the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research pape.展开更多
文摘Measurement of optical properties of skin is an expanding and growing field of research.Recent studies have shown that the biological tissue,especially skin,changes the polarization state of the incident light.Using this property will enable the study of abnormalities and diseases that alter not only the light intensity but also its polarization state.In this paper we report an experimental study for measuring changes of polarization state of the light scattered from a phantom similar to a sample model of scattering skin.Using the notation of Stokes vector for the polarized light and Mueller matrix for the sample with its polarization properties,we have shown that some elements of the matrix were particularly sensitive to the changes of the polarization-altering physical properties of the scatterers within the phantom.
文摘Post-translational modifications(PTMs)have key roles in extending the functional diversity of proteins and,as a result,regulating diverse cellular processes in prokaryotic and eukaryotic organisms.Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes.Disorders in the phosphorylation process lead to multiple diseases,including neurological disorders and cancers.The purpose of this review is to organize this body of knowledge associated with phosphorylation site(p-site)prediction to facilitate future research in this field.At first,we comprehensively review all related databases and introduce all steps regarding dataset creation,data preprocessing,and method evaluation in p-site prediction.Next,we investigate p-site prediction methods,which are divided into two computational groups:algorithmic and machine learning(ML).Additionally,it is shown that there are basically two main approaches for p-site prediction by ML:conventional and end-to-end deep learning methods,both of which are given an overview.Moreover,this review introduces the most important feature extraction techniques,which have mostly been used in p-site prediction.Finally,we create three test sets from new proteins related to the released version of the database of protein post-translational modifications(dbPTM)in 2022 based on general and human species.Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release,distinct from those in the dbPTM 2019 release,reveals their limitations.In other words,the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research pape.
