Mutations in Eml1 lead to ectopic progenitors and neuronal heterotopia in mouse and human.

Neuronal migration disorders such as lissencephaly and subcortical band heterotopia are associated with epilepsy and intellectual disability. DCX, PAFAH1B1 and TUBA1A are mutated in these disorders; however, corresponding mouse mutants do not show heterotopic neurons in the neocortex. In contrast, spontaneously arisen HeCo mice display this phenotype, and our study revealed that misplaced apical progenitors contribute to heterotopia formation. While HeCo neurons migrated at the same speed as wild type, abnormally distributed dividing progenitors were found throughout the cortical wall from embryonic day 13. We identified Eml1, encoding a microtubule-associated protein, as the gene mutated in HeCo mice. Full-length transcripts were lacking as a result of a retrotransposon insertion in an intron. Eml1 knockdown mimicked the HeCo progenitor phenotype and reexpression rescued it. We further found EML1 to be mutated in ribbon-like heterotopia in humans. Our data link abnormal spindle orientations, ectopic progenitors and severe heterotopia in mouse and human

Assimilation of surface current measurements in a coastal ocean model

An idealized, linear model of the coastal ocean is used to assess the domain of influence of surface type data, in particular how much information such data contain about the ocean state at depth and how such information may be retrieved. The ultimate objective is to assess the feasibility of assimilation of real surface current data, obtained from coastal radar measurements, into more realistic dynamical models. The linear model is used here with a variational inverse assimilation scheme, which is optimal in the sense that under appropriate assumptions about the errors, the maximum possible information is retrieved from the surface data. A comparison is made between strongly and weakly constrained variational formulations. The use of a linear model permits significant analytic progress. Analysis is presented for the solution of the inverse problem by expanding in terms of representer functions, greatly reducing the dimension of the solution space without compromising the optimization. The representer functions also provide important information about the domain of influence of each data point, about optimal location and resolution of the data points, about the error statistics of the inverse solution itself, and how that depends upon the error statistics of the data and of the model. Finally, twin experiments illustrate how well a known ocean state can be reconstructed from sampled data. Consideration of the statistics of an ensemble of such twin experiments provides insight into the dependence of the inverse solution on the choice of weights, on the data error, and on the sampling resolution.</p

Assimilation of surface current measurements in a coastal ocean model

An idealized, linear model of the coastal ocean is used to assess the domain of influence of surface type data, in particular how much information such data contain about the ocean state at depth and how such information may be retrieved. The ultimate objective is to assess the feasibility of assimilation of real surface current data, obtained from coastal radar measurements, into more realistic dynamical models. The linear model is used here with a variational inverse assimilation scheme, which is optimal in the sense that under appropriate assumptions about the errors, the maximum possible information is retrieved from the surface data. A comparison is made between strongly and weakly constrained variational formulations. The use of a linear model permits significant analytic progress. Analysis is presented for the solution of the inverse problem by expanding in terms of representer functions, greatly reducing the dimension of the solution space without compromising the optimization. The representer functions also provide important information about the domain of influence of each data point, about optimal location and resolution of the data points, about the error statistics of the inverse solution itself, and how that depends upon the error statistics of the data and of the model. Finally, twin experiments illustrate how well a known ocean state can be reconstructed from sampled data. Consideration of the statistics of an ensemble of such twin experiments provides insight into the dependence of the inverse solution on the choice of weights, on the data error, and on the sampling resolution.</p