Lisfranc injuries can be difficult injuries to identify and treat, while also being the subject of significant debate on proper surgical management. A narrative literature review was performed using Pubmed and Google ...Lisfranc injuries can be difficult injuries to identify and treat, while also being the subject of significant debate on proper surgical management. A narrative literature review was performed using Pubmed and Google Scholar databases to identify recent studies evaluating open reduction internal fixation vs primary arthrodesis for Lisfranc injuries to further elucidate optimal surgical management. Additional focus was placed removal of hardware after ORIF to identify the need for routine hardware removal as an additional surgery may guide surgeon decision-making. This review showed inconclusive data on the superiority of ORIF vs arthrodesis, as multiple conflicting results exist, though established that functional results are similar between these options. Though both are generally accepted treatment options, there are no well-designed randomized controlled trials directly comparing the two. Retention of hardware after ORIF has been shown to be tolerated, though there is a significant risk of the need for unplanned removal due to pain and hardware breakage.展开更多
In this paper, we focus on a type of inverse problem in which the data are expressed as an unknown function of the sought and unknown model function (or its discretised representation as a model parameter vector). In ...In this paper, we focus on a type of inverse problem in which the data are expressed as an unknown function of the sought and unknown model function (or its discretised representation as a model parameter vector). In particular, we deal with situations in which training data are not available. Then we cannot model the unknown functional relationship between data and the unknown model function (or parameter vector) with a Gaussian Process of appropriate dimensionality. A Bayesian method based on state space modelling is advanced instead. Within this framework, the likelihood is expressed in terms of the probability density function (pdf) of the state space variable and the sought model parameter vector is embedded within the domain of this pdf. As the measurable vector lives only inside an identified sub-volume of the system state space, the pdf of the state space variable is projected onto the space of the measurables, and it is in terms of the projected state space density that the likelihood is written;the final form of the likelihood is achieved after convolution with the distribution of measurement errors. Application motivated vague priors are invoked and the posterior probability density of the model parameter vectors, given the data are computed. Inference is performed by taking posterior samples with adaptive MCMC. The method is illustrated on synthetic as well as real galactic data.展开更多
Aims The potential for mixtures of plant species to produce more biomass than every one of their constituent species in monoculture is still controversially discussed in the literature.Here we tested how this socalled...Aims The potential for mixtures of plant species to produce more biomass than every one of their constituent species in monoculture is still controversially discussed in the literature.Here we tested how this socalled transgressive overyielding is affected by variation between and within species in monoculture yields in biodiversity experiments.Methods We use basic statistical principles to calculate expected maximum monoculture yield in a species pool used for a biodiversity experiment.Using a real example we show how between-and withinspecies variance components in monoculture yields can be obtained.Combining the two components we estimate the importance of sampling bias in transgressive overyielding analysis.Important Findings The net biodiversity effect(difference between mixture and average monoculture yield)needed to achieve transgressive overyielding increases with the number of species in a mixture and with the variation between constituent species in monoculture yields.If there is no significant variation between species,transgressive overyielding should not be calculated using the best monoculture,because in this case the difference between this species and the other species could exclusively reflect a sampling bias.The sampling bias decreases with increasing variation between species.Tests for transgressive overyielding require replicated species’monocultures.However,it can be doubted whether such an emphasis on monocultures in biodiversity experiments is justified if an analysis of transgressive overyielding is not the major goal.展开更多
文摘Lisfranc injuries can be difficult injuries to identify and treat, while also being the subject of significant debate on proper surgical management. A narrative literature review was performed using Pubmed and Google Scholar databases to identify recent studies evaluating open reduction internal fixation vs primary arthrodesis for Lisfranc injuries to further elucidate optimal surgical management. Additional focus was placed removal of hardware after ORIF to identify the need for routine hardware removal as an additional surgery may guide surgeon decision-making. This review showed inconclusive data on the superiority of ORIF vs arthrodesis, as multiple conflicting results exist, though established that functional results are similar between these options. Though both are generally accepted treatment options, there are no well-designed randomized controlled trials directly comparing the two. Retention of hardware after ORIF has been shown to be tolerated, though there is a significant risk of the need for unplanned removal due to pain and hardware breakage.
文摘In this paper, we focus on a type of inverse problem in which the data are expressed as an unknown function of the sought and unknown model function (or its discretised representation as a model parameter vector). In particular, we deal with situations in which training data are not available. Then we cannot model the unknown functional relationship between data and the unknown model function (or parameter vector) with a Gaussian Process of appropriate dimensionality. A Bayesian method based on state space modelling is advanced instead. Within this framework, the likelihood is expressed in terms of the probability density function (pdf) of the state space variable and the sought model parameter vector is embedded within the domain of this pdf. As the measurable vector lives only inside an identified sub-volume of the system state space, the pdf of the state space variable is projected onto the space of the measurables, and it is in terms of the projected state space density that the likelihood is written;the final form of the likelihood is achieved after convolution with the distribution of measurement errors. Application motivated vague priors are invoked and the posterior probability density of the model parameter vectors, given the data are computed. Inference is performed by taking posterior samples with adaptive MCMC. The method is illustrated on synthetic as well as real galactic data.
基金German Science Foundation(FOR 456–WE 2618/6-1 to B.S.)Swiss National Science Foundation(31–65224.01 to B.S.)Natural Sciences and Engineering Research Council of Canada(M.L.)。
文摘Aims The potential for mixtures of plant species to produce more biomass than every one of their constituent species in monoculture is still controversially discussed in the literature.Here we tested how this socalled transgressive overyielding is affected by variation between and within species in monoculture yields in biodiversity experiments.Methods We use basic statistical principles to calculate expected maximum monoculture yield in a species pool used for a biodiversity experiment.Using a real example we show how between-and withinspecies variance components in monoculture yields can be obtained.Combining the two components we estimate the importance of sampling bias in transgressive overyielding analysis.Important Findings The net biodiversity effect(difference between mixture and average monoculture yield)needed to achieve transgressive overyielding increases with the number of species in a mixture and with the variation between constituent species in monoculture yields.If there is no significant variation between species,transgressive overyielding should not be calculated using the best monoculture,because in this case the difference between this species and the other species could exclusively reflect a sampling bias.The sampling bias decreases with increasing variation between species.Tests for transgressive overyielding require replicated species’monocultures.However,it can be doubted whether such an emphasis on monocultures in biodiversity experiments is justified if an analysis of transgressive overyielding is not the major goal.
