Pub Date : 2012-10-05DOI: 10.11606/T.10.2012.tde-24042014-075742
G. Filho, Jos e
A network is a set of nodes that are linked together by a set of edges. Networks can represent any set of objects that have relations among themselves. Communities are sets of nodes that are related in an important way, probably sharing common properties and/or playing similar roles within a network. When network analysis is applied to study the livestock movement patterns, the epidemiological units of interest (farm premises, counties, states, countries, etc.) are represented as nodes, and animal movements between the nodes are represented as the edges of a network. Unraveling a network structure, and hence the trade preferences and pathways, could be very useful to a researcher or a decision-maker. We implemented a community detection algorithm to find livestock communities that is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its life time than expected by chance. We applied this algorithm to the network of within animal movements made inside the State of Mato Grosso, for the year of 2007. This database holds information about 87,899 premises and 521,431 movements throughout the year, totalizing 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features to preventive veterinary medicine applications, and also has a meaningful interpretation in trade networks where links emerge from the choice of trader nodes.
{"title":"Caracterização de circuitos pecuários com base em redes de movimentação de animais","authors":"G. Filho, Jos e","doi":"10.11606/T.10.2012.tde-24042014-075742","DOIUrl":"https://doi.org/10.11606/T.10.2012.tde-24042014-075742","url":null,"abstract":"A network is a set of nodes that are linked together by a set of edges. Networks can represent any set of objects that have relations among themselves. Communities are sets of nodes that are related in an important way, probably sharing common properties and/or playing similar roles within a network. When network analysis is applied to study the livestock movement patterns, the epidemiological units of interest (farm premises, counties, states, countries, etc.) are represented as nodes, and animal movements between the nodes are represented as the edges of a network. Unraveling a network structure, and hence the trade preferences and pathways, could be very useful to a researcher or a decision-maker. We implemented a community detection algorithm to find livestock communities that is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its life time than expected by chance. We applied this algorithm to the network of within animal movements made inside the State of Mato Grosso, for the year of 2007. This database holds information about 87,899 premises and 521,431 movements throughout the year, totalizing 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features to preventive veterinary medicine applications, and also has a meaningful interpretation in trade networks where links emerge from the choice of trader nodes.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115280164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study kinetic models of reversible enzyme reactions and compare two techniques for analytic approximate solutions of the model. Analytic approximate solutions of non-linear reaction equations for reversible enzyme reactions are calculated using the Homotopy Perturbation Method (HPM) and the Simple Iteration Method (SIM). The results of the approximations are similar. The Matlab programs are included in appendices.
{"title":"Iterative Approximate Solutions of Kinetic Equations for Reversible Enzyme Reactions","authors":"S. Khoshnaw","doi":"10.4236/NS.2013.56091","DOIUrl":"https://doi.org/10.4236/NS.2013.56091","url":null,"abstract":"We study kinetic models of reversible enzyme reactions and compare two techniques for analytic approximate solutions of the model. Analytic approximate solutions of non-linear reaction equations for reversible enzyme reactions are calculated using the Homotopy Perturbation Method (HPM) and the Simple Iteration Method (SIM). The results of the approximations are similar. The Matlab programs are included in appendices.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129576734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.
{"title":"Ising Models for Inferring Network Structure From Spike Data","authors":"J. Hertz, Y. Roudi, J. Tyrcha","doi":"10.1201/B14756-31","DOIUrl":"https://doi.org/10.1201/B14756-31","url":null,"abstract":"Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128976378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-27DOI: 10.1038/NPRE.2011.5927.1
Simeon Cole-Fletcher, Lucas Marin-Salcedo, A. Rana, M. Courtney
{"title":"Errors in Length-weight Parameters at FishBase.org","authors":"Simeon Cole-Fletcher, Lucas Marin-Salcedo, A. Rana, M. Courtney","doi":"10.1038/NPRE.2011.5927.1","DOIUrl":"https://doi.org/10.1038/NPRE.2011.5927.1","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126836623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many cellular behaviors are regulated by gene regulation networks, kinetics of which is one of the main subjects in the study of systems biology. Because of the low number molecules in these reacting systems, stochastic effects are significant. In recent years, stochasticity in modeling the kinetics of gene regulation networks have been drawing the attention of many researchers. This paper is a self contained review trying to provide an overview of stochastic modeling. I will introduce the derivation of the main equations in modeling the biochemical systems with intrinsic noise (chemical master equation, Fokker-Plan equation, reaction rate equation, chemical Langevin equation), and will discuss the relations between these formulations. The mathematical formulations for systems with fluctuations in kinetic parameters are also discussed. Finally, I will introduce the exact stochastic simulation algorithm and the approximate explicit tau-leaping method for making numerical simulations.
{"title":"Stochastic Modeling in Systems Biology","authors":"J. Lei","doi":"10.1166/JAMA.2012.1007","DOIUrl":"https://doi.org/10.1166/JAMA.2012.1007","url":null,"abstract":"Many cellular behaviors are regulated by gene regulation networks, kinetics of which is one of the main subjects in the study of systems biology. Because of the low number molecules in these reacting systems, stochastic effects are significant. In recent years, stochasticity in modeling the kinetics of gene regulation networks have been drawing the attention of many researchers. This paper is a self contained review trying to provide an overview of stochastic modeling. I will introduce the derivation of the main equations in modeling the biochemical systems with intrinsic noise (chemical master equation, Fokker-Plan equation, reaction rate equation, chemical Langevin equation), and will discuss the relations between these formulations. The mathematical formulations for systems with fluctuations in kinetic parameters are also discussed. Finally, I will introduce the exact stochastic simulation algorithm and the approximate explicit tau-leaping method for making numerical simulations.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130444565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The process controlling the diferentiation of stem, or progenitor, cells into one specific functional direction is called lineage specification. An important characteristic of this process is the multi-lineage priming, which requires the simultaneous expression of lineage-specific genes. Prior to commitment to a certain lineage, it has been observed that these genes exhibit intermediate values of their expression levels. Multi-lineage differentiation has been reported for various progenitor cells, and it has been explained through the bifurcation of a metastable state. During the differentiation process the dynamics of the core regulatory network follows a bifurcation, where the metastable state, corresponding to the progenitor cell, is destabilized and the system is forced to choose between the possible developmental alternatives. While this approach gives a reasonable interpretation of the cell fate decision process, it fails to explain the multi-lineage priming characteristic. Here, we describe a new multi-dimensional switch-like model that captures both the process of cell fate decision and the phenomenon of multi-lineage priming. We show that in the symmetrical interaction case, the system exhibits a new type of degenerate bifurcation, characterized by a critical hyperplane, containing an infinite number of critical steady states. This critical hyperplane may be interpreted as the support for the multi-lineage priming states of the progenitor. Also, the cell fate decision (the multi-stability and switching behavior) can be explained by a symmetry breaking in the parameter space of this critical hyperplane. These analytical results are confirmed by Monte-Carlo simulations of the corresponding chemical master equations.
{"title":"Monte-Carlo Simulation of a Multi-Dimensional Switch-Like Model of Stem Cell Differentiation","authors":"M. Andrecut","doi":"10.5772/15474","DOIUrl":"https://doi.org/10.5772/15474","url":null,"abstract":"The process controlling the diferentiation of stem, or progenitor, cells into one specific functional direction is called lineage specification. An important characteristic of this process is the multi-lineage priming, which requires the simultaneous expression of lineage-specific genes. Prior to commitment to a certain lineage, it has been observed that these genes exhibit intermediate values of their expression levels. Multi-lineage differentiation has been reported for various progenitor cells, and it has been explained through the bifurcation of a metastable state. During the differentiation process the dynamics of the core regulatory network follows a bifurcation, where the metastable state, corresponding to the progenitor cell, is destabilized and the system is forced to choose between the possible developmental alternatives. While this approach gives a reasonable interpretation of the cell fate decision process, it fails to explain the multi-lineage priming characteristic. Here, we describe a new multi-dimensional switch-like model that captures both the process of cell fate decision and the phenomenon of multi-lineage priming. We show that in the symmetrical interaction case, the system exhibits a new type of degenerate bifurcation, characterized by a critical hyperplane, containing an infinite number of critical steady states. This critical hyperplane may be interpreted as the support for the multi-lineage priming states of the progenitor. Also, the cell fate decision (the multi-stability and switching behavior) can be explained by a symmetry breaking in the parameter space of this critical hyperplane. These analytical results are confirmed by Monte-Carlo simulations of the corresponding chemical master equations.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sample, two properties which make it well suited for association studies. Sparse Partitioning differs from other regression methods by placing no restrictions on how the predictors may influence the response. To compensate for this generality, Sparse Partitioning implements a novel way of exploring the model space. It searches for high posterior probability partitions of the predictor set, where each partition defines groups of predictors that jointly influence the response. The result is a robust method that requires no prior knowledge of the true predictor--response relationship. Testing on simulated data suggests Sparse Partitioning will typically match the performance of an existing method on a data set which obeys the existing method's model assumptions. When these assumptions are violated, Sparse Partitioning will generally offer superior performance.
{"title":"Sparse Partitioning: Nonlinear regression with binary or tertiary predictors, with application to association studies","authors":"D. Speed, Simon Tavar'e","doi":"10.1214/10-AOAS411","DOIUrl":"https://doi.org/10.1214/10-AOAS411","url":null,"abstract":"This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sample, two properties which make it well suited for association studies. Sparse Partitioning differs from other regression methods by placing no restrictions on how the predictors may influence the response. To compensate for this generality, Sparse Partitioning implements a novel way of exploring the model space. It searches for high posterior probability partitions of the predictor set, where each partition defines groups of predictors that jointly influence the response. The result is a robust method that requires no prior knowledge of the true predictor--response relationship. Testing on simulated data suggests Sparse Partitioning will typically match the performance of an existing method on a data set which obeys the existing method's model assumptions. When these assumptions are violated, Sparse Partitioning will generally offer superior performance.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125949977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Sotolongo-Grau, Daniel Rodr'iguez-P'erez, J. Antoranz, O. Sotolongo-Costa
The expression of survival factors for radiation damaged cells is based on probabilistic assumptions and experimentally fitted for each tumor, radiation and conditions. Here we show how the simplest of these radiobiological models can be derived from the maximum entropy principle of the classical Boltzmann-Gibbs expression. We extend this derivation using the Tsallis entropy and a cutoff hypothesis, motivated by clinical observations. A generalization of the exponential, the logarithm and the product to a non-extensive framework, provides a simple formula for the survival fraction corresponding to the application of several radiation doses on a living tissue. The obtained expression shows a remarkable agreement with the experimental data found in the literature, also providing a new interpretation of some of the parameters introduced anew. It is also shown how the presented formalism may has direct application in radiotherapy treatment optimization through the definition of the potential effect difference, simply calculated between the tumour and the surrounding tissue.
{"title":"Non-extensive radiobiology","authors":"O. Sotolongo-Grau, Daniel Rodr'iguez-P'erez, J. Antoranz, O. Sotolongo-Costa","doi":"10.1063/1.3573620","DOIUrl":"https://doi.org/10.1063/1.3573620","url":null,"abstract":"The expression of survival factors for radiation damaged cells is based on probabilistic assumptions and experimentally fitted for each tumor, radiation and conditions. Here we show how the simplest of these radiobiological models can be derived from the maximum entropy principle of the classical Boltzmann-Gibbs expression. We extend this derivation using the Tsallis entropy and a cutoff hypothesis, motivated by clinical observations. A generalization of the exponential, the logarithm and the product to a non-extensive framework, provides a simple formula for the survival fraction corresponding to the application of several radiation doses on a living tissue. The obtained expression shows a remarkable agreement with the experimental data found in the literature, also providing a new interpretation of some of the parameters introduced anew. It is also shown how the presented formalism may has direct application in radiotherapy treatment optimization through the definition of the potential effect difference, simply calculated between the tumour and the surrounding tissue.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114856702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially expressed genes between two patient populations, as shifts in expression levels are expected to be coherent with the structure of graphs reflecting gene properties such as biological process, molecular function, regulation, or metabolism. For a fixed graph of interest, we demonstrate that accounting for graph structure can yield more powerful tests under the assumption of smooth distribution shift on the graph. We also investigate the identification of non-homogeneous subgraphs of a given large graph, which poses both computational and multiple testing problems. The relevance and benefits of the proposed approach are illustrated on synthetic data and on breast cancer gene expression data analyzed in context of KEGG pathways.
{"title":"Gains in Power from Structured Two-Sample Tests of Means on Graphs","authors":"Laurent Jacob, P. Neuvial, S. Dudoit","doi":"10.1214/11-AOAS528","DOIUrl":"https://doi.org/10.1214/11-AOAS528","url":null,"abstract":"We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially expressed genes between two patient populations, as shifts in expression levels are expected to be coherent with the structure of graphs reflecting gene properties such as biological process, molecular function, regulation, or metabolism. For a fixed graph of interest, we demonstrate that accounting for graph structure can yield more powerful tests under the assumption of smooth distribution shift on the graph. We also investigate the identification of non-homogeneous subgraphs of a given large graph, which poses both computational and multiple testing problems. The relevance and benefits of the proposed approach are illustrated on synthetic data and on breast cancer gene expression data analyzed in context of KEGG pathways.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-20DOI: 10.1002/9781118131374.CH13
O. Flomenbom
Deducing an underlying multi-substate on-off kinetic scheme (KS) from the statistical properties of a two-state trajectory is the aim from many experiments in biophysics and chemistry, such as, ion channel recordings, enzymatic activity and structural dynamics of bio-molecules. Doing so is almost always impossible, as the mapping of a KS into a two-state trajectory leads to the loss of information about the KS (almost always). Here, we present the optimal way to solve this problem. It is based on unique forms of reduced dimensions (RD). RD forms are on-off networks with connections only between substates of different states, where the connections can have multi-exponential waiting time probability density functions (WT-PDFs). A RD form has the simplest toplogy that can reproduce a given data. In theory, only a single RD form can be constructed from the full data (hence its uniqueness), still this task is not easy when dealing with finite data. For doing so, a toolbox made of known statistical methods in data analysis and new statistical methods and numerical algorithms develped for this problem is presented. Our toolbox is self-contained: it builds a mechanism based only on the information it extracts from the data. The implementation of the toolbox on the data is fast. The toolbox is automated and is available for academic research upon electronic request.
{"title":"Making it Possible: Constructing a Reliable Mechanism from a Finite Trajectory","authors":"O. Flomenbom","doi":"10.1002/9781118131374.CH13","DOIUrl":"https://doi.org/10.1002/9781118131374.CH13","url":null,"abstract":"Deducing an underlying multi-substate on-off kinetic scheme (KS) from the statistical properties of a two-state trajectory is the aim from many experiments in biophysics and chemistry, such as, ion channel recordings, enzymatic activity and structural dynamics of bio-molecules. Doing so is almost always impossible, as the mapping of a KS into a two-state trajectory leads to the loss of information about the KS (almost always). Here, we present the optimal way to solve this problem. It is based on unique forms of reduced dimensions (RD). RD forms are on-off networks with connections only between substates of different states, where the connections can have multi-exponential waiting time probability density functions (WT-PDFs). A RD form has the simplest toplogy that can reproduce a given data. In theory, only a single RD form can be constructed from the full data (hence its uniqueness), still this task is not easy when dealing with finite data. For doing so, a toolbox made of known statistical methods in data analysis and new statistical methods and numerical algorithms develped for this problem is presented. Our toolbox is self-contained: it builds a mechanism based only on the information it extracts from the data. The implementation of the toolbox on the data is fast. The toolbox is automated and is available for academic research upon electronic request.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131134013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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