In this paper, epidemic spread with the staged progression model on homogeneous and heterogeneous networks is studied. First, the epidemic threshold of the simple staged progression model is given. Then the staged pro...In this paper, epidemic spread with the staged progression model on homogeneous and heterogeneous networks is studied. First, the epidemic threshold of the simple staged progression model is given. Then the staged progression model with birth and death is also considered. The case where infectivity is a nonlinear function of the nodes＇ degree is discussed, too. Finally, the analytical results are verified by numerical simulations.展开更多
In this paper,we analyze a higher-order stochastically perturbed multigroup staged-progression model for the transmission of HlV with saturated incidence rate.We obtainsufficient conditions for the existence and uniqu...In this paper,we analyze a higher-order stochastically perturbed multigroup staged-progression model for the transmission of HlV with saturated incidence rate.We obtainsufficient conditions for the existence and uniqueness of an ergodic stationary distribu-tion of positive solutions to the system by establishing a suitable stochastic Lyapunovfunction.In addition,we make up adequate conditions for complete eradication and wip-ing out the infectious disease.In a biological interpretation,the existence of a stationarydistribution implies that the disease will prevail and persist in the long term.Finally,examples and numerical simulations are introduced to validate our theoretical results.展开更多
Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individua...Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice.Thus,it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context.Here,we developed a new computational framework,EdgeBiomarker,to integrate edge and node biomarkers to diagnose phenotype of each single test sample.By applying the method to datasets of lung and breast cancer,it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages.Our method shows advantages over traditional methods:(i)edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes,suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods;(ii)edge biomarkers categorize patients into low/high survival rate in a more reliablemanner;(iii)edge biomarkers are significantly enriched in relevant biological functions or pathways,implying that the association changes ina network,rather than expression changes in individual molecules,tendtobe causally related to cancer development.The new frameworkof edgebiomarkers paves theway for diagnosing diseases and analyzing the irmolecular mechanisms by edges or networks in one-sample-for-one-individual basis.This also provides a powerful tool for precision medicine or big-data medicine.展开更多
Neurodegenerative diseases are characterized by a progressive dysfunction of the nervous system.Often associated with atrophy of the affected central or peripheral nervous structures,they include diseases such as Park...Neurodegenerative diseases are characterized by a progressive dysfunction of the nervous system.Often associated with atrophy of the affected central or peripheral nervous structures,they include diseases such as Parkinson’s Disease(PD),Alzheimer’s Disease and other dementias,Genetic Brain Disorders,Amyotrophic Lateral Sclerosis(ALS or Lou Gehrig’s Disease),Huntington’s Disease,Prion Diseases,and others.The prevalence of neurodegenerative diseases has increased over the last years.This has had a major impact both on patients and their families and has exponentially increased the medical bill by hundreds of billions of Euros.Therefore,understanding the role of environmental and genetic factors in the pathogenesis of PD is crucial to develop preventive strategies.While some authors believe that PD is mainly genetic and that the aging of the society is the principal cause for this increase,different studies suggest that PD may be due to an increased exposure to environmental toxins.In this article we review epidemiological,sociological and experimental studies to determine which hypothesis is more plausible.Our conclusion is that,at least in idiopathic PD(iPD),the exposure to toxic environmental substances could play an important role in its aetiology.展开更多
Prostate cancer is one of the most common cancers in men worldwide, and the number of diagnosed patients has dramatically increased in recent years. Currently, the clinical parameters used to diagnose prostate cancer,...Prostate cancer is one of the most common cancers in men worldwide, and the number of diagnosed patients has dramatically increased in recent years. Currently, the clinical parameters used to diagnose prostate cancer, such as Gleason score, pathological tumor staging, and prostate-specific antigen(PSA) expression level, are considered insufficient to inform recommendation to guide clinical practice. Thus, identification of a novel biomarker is necessary. TWIST is one of the well-studied targets and is correlated with cancer invasion and metastasis in several human cancers. We have investigated two largest prostate cancer patient cohorts available in GEO database and found that TWIST expression is positive correlated with Gleason score and associated with poorer survival. By using a prostate cancer cohort and a prostate cancer cell line dataset, we have identified three potential downstream targets of TWIST, PPM1 A, SRP72 and TBCB. TWIST's prognostic capacity is lost when the gene is mutated. Further investigation in the prostate cancer cohort revealed that gene expression of SERPINA, STX7, PDIA2, FMP5, GP1 BB, VGLL4,KCNMA1, SHMT2, SAA4 and DIDO1 influence the prognostic significance of TWIST and vice versa. Importantly, eight out of these ten genes are prognostic indicator by itself. In conclusion, our study has further confirmed that TWIST is a prognostic marker in prostate cancer, identified its potential downstream targets and genes that could possibly give additional prognostic value to predict TWIST-mediated prostate cancer progression.展开更多
基金supported by Hong Kong Polytechnic University Grant via a Council Competitive Earmarked Research Grant(CERG) under Grant No.PolyU 5279//08Ethe National Natural Science Foundation of China under Grant Nos.11005001 and 11072136+1 种基金the 211 Project of Anhui University under Grant No.2009QN003A,KJTD002Bsupported by Shanghai Leading Academic Discipline Project under Grant No.S30104
文摘In this paper, epidemic spread with the staged progression model on homogeneous and heterogeneous networks is studied. First, the epidemic threshold of the simple staged progression model is given. Then the staged progression model with birth and death is also considered. The case where infectivity is a nonlinear function of the nodes＇ degree is discussed, too. Finally, the analytical results are verified by numerical simulations.
基金This work is supported by the National Natural Science Foundation of China(Nos.12001090 and 11871473)Shandong Provincial Natural Science Foundation(No.ZR2019MA010)the Fundamental Research Funds for the Central Universitiesof China(No.2412020QD024).
文摘In this paper,we analyze a higher-order stochastically perturbed multigroup staged-progression model for the transmission of HlV with saturated incidence rate.We obtainsufficient conditions for the existence and uniqueness of an ergodic stationary distribu-tion of positive solutions to the system by establishing a suitable stochastic Lyapunovfunction.In addition,we make up adequate conditions for complete eradication and wip-ing out the infectious disease.In a biological interpretation,the existence of a stationarydistribution implies that the disease will prevail and persist in the long term.Finally,examples and numerical simulations are introduced to validate our theoretical results.
基金This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)(No.XDB13040700)the National Program on Key Basic Research Project(No.2014CB910504)+1 种基金the National Natural Science Foundation of China(No.91439103,61134013,31200987)the Knowledge Innovation Program of SIBS of CAS(No.2013KIP218).
文摘Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However,obtaining edges orcorrelations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice.Thus,it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context.Here,we developed a new computational framework,EdgeBiomarker,to integrate edge and node biomarkers to diagnose phenotype of each single test sample.By applying the method to datasets of lung and breast cancer,it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages.Our method shows advantages over traditional methods:(i)edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes,suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods;(ii)edge biomarkers categorize patients into low/high survival rate in a more reliablemanner;(iii)edge biomarkers are significantly enriched in relevant biological functions or pathways,implying that the association changes ina network,rather than expression changes in individual molecules,tendtobe causally related to cancer development.The new frameworkof edgebiomarkers paves theway for diagnosing diseases and analyzing the irmolecular mechanisms by edges or networks in one-sample-for-one-individual basis.This also provides a powerful tool for precision medicine or big-data medicine.
文摘Neurodegenerative diseases are characterized by a progressive dysfunction of the nervous system.Often associated with atrophy of the affected central or peripheral nervous structures,they include diseases such as Parkinson’s Disease(PD),Alzheimer’s Disease and other dementias,Genetic Brain Disorders,Amyotrophic Lateral Sclerosis(ALS or Lou Gehrig’s Disease),Huntington’s Disease,Prion Diseases,and others.The prevalence of neurodegenerative diseases has increased over the last years.This has had a major impact both on patients and their families and has exponentially increased the medical bill by hundreds of billions of Euros.Therefore,understanding the role of environmental and genetic factors in the pathogenesis of PD is crucial to develop preventive strategies.While some authors believe that PD is mainly genetic and that the aging of the society is the principal cause for this increase,different studies suggest that PD may be due to an increased exposure to environmental toxins.In this article we review epidemiological,sociological and experimental studies to determine which hypothesis is more plausible.Our conclusion is that,at least in idiopathic PD(iPD),the exposure to toxic environmental substances could play an important role in its aetiology.
基金supported by the University of Macao Multi-Year Research Grants (MYRG2015-00065FHS)the Macao Science and Technology Development Fund (FDCT 018-2015-A1) to Dr. Hang Fai Kwok research group
文摘Prostate cancer is one of the most common cancers in men worldwide, and the number of diagnosed patients has dramatically increased in recent years. Currently, the clinical parameters used to diagnose prostate cancer, such as Gleason score, pathological tumor staging, and prostate-specific antigen(PSA) expression level, are considered insufficient to inform recommendation to guide clinical practice. Thus, identification of a novel biomarker is necessary. TWIST is one of the well-studied targets and is correlated with cancer invasion and metastasis in several human cancers. We have investigated two largest prostate cancer patient cohorts available in GEO database and found that TWIST expression is positive correlated with Gleason score and associated with poorer survival. By using a prostate cancer cohort and a prostate cancer cell line dataset, we have identified three potential downstream targets of TWIST, PPM1 A, SRP72 and TBCB. TWIST's prognostic capacity is lost when the gene is mutated. Further investigation in the prostate cancer cohort revealed that gene expression of SERPINA, STX7, PDIA2, FMP5, GP1 BB, VGLL4,KCNMA1, SHMT2, SAA4 and DIDO1 influence the prognostic significance of TWIST and vice versa. Importantly, eight out of these ten genes are prognostic indicator by itself. In conclusion, our study has further confirmed that TWIST is a prognostic marker in prostate cancer, identified its potential downstream targets and genes that could possibly give additional prognostic value to predict TWIST-mediated prostate cancer progression.
