18 research outputs found
Congenital mirror movements in a new Italian family
Mirror movements (MMs) occur on the contralateral side of a limb being used intentionally.
Because few families with congenital MMs and no other neurological signs have been reported, the underlying
mechanisms of MMs are still not entirely clear. We report on the clinical, genetic, neurophysiological and
neuroimaging findings of 10 of 26 living members of a novel four-generation family with congenital MMs. DCC
and RAD51 were sequenced in affected members of the family. Five of the ten subjects with MMs underwent
neurophysiological and neuroimaging evaluations. The neurophysiological evaluation consisted of
electromyographic (EMG) mirror recordings, investigations of corticospinal excitability, and analysis of
interhemispheric inhibition using transcranial magnetic stimulation techniques. The neuroimaging evaluation
included functional MRI during finger movements. Eight (all females) of the ten members examined presented
MMs of varying degrees at the clinical assessment. Transmission of MMs appears to have occurred according
to an autosomal-dominant fashion with variable expression. No mutation in DCC or RAD51 was identified. EMG
mirror activity was higher in MM subjects than in healthy controls. Short-latency interhemispheric inhibition
was reduced in MM subjects. Ipsilateral motor-evoked potentials were detectable in the most severe case.
The neuroimaging evaluation did not disclose any significant abnormalities in MM subjects. The variability of
the clinical features of this family, and the lack of known genetic abnormalities, suggests that MMs are
heterogeneous disorders. The pathophysiological mechanisms of MMs include abnormalities of transcallosal
inhibition and corticospinal decussatio
How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics
Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of âreproducible researchâ in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics
How to make a pie? Reproducible Research for Empirical Economics & Econometrics
Les recherches empiriques en sciences humaines et sociales nĂ©cessitent la manipulation de nombreux fichiers : diffĂ©rents jeux de donnĂ©es, de multiples programmes, qu'ils soient destinĂ©s Ă la manipulation de donnĂ©es, aux traitements statistiques, aux estimations Ă©conomĂ©triques ou Ă des simulations, et de nombreux fichiers successifs de rĂ©sultats.Maitriser lâensemble des Ă©tapes du projet de recherche est indispensable si lâon souhaite pouvoir reproduire ou rĂ©pliquer les rĂ©sultats Ă long terme. Cette rigueur (fiabilitĂ©, traçabilitĂ©, reproductibilitĂ©), est dĂ©sormais de plus en plus exigĂ©e par notre profession ainsi que par les Ă©diteurs de revues scientifiques.AprĂšs avoir mis en Ă©vidence les enjeux de cette « recherche reproductible » (Claerbout, 1990) pour les Ă©conomistes, nous en dĂ©clinons les principes autour de trois axes simples : organiser les Ă©tapes du projet et les fichiers, Ă©crire des programmes clairs et documentĂ©s, et automatiser le plus possible les opĂ©rations jusquâau document prĂ©sentant les rĂ©sultats. Ces principes sont alors illustrĂ©s par diffĂ©rentes bonnes pratiques, en allant des plus simples au plus sophistiquĂ©es, avec un focus particulier sur les fonctionnalitĂ©s des logiciels les plus courants en Ă©conomie (Stata, R, SAS, Matlab, Mathematica, Gams)
How to make a pie: Reproductible research for empirical economics and econometrics
Empirical economics and econometrics (EEE) research now relies primarily on the application of code to data sets. Handling the workflow that links data sets, programs, results and finally manuscript(s) is essential if one wishes to reproduce results. Herein, we highlight the importance of "reproducible research" in EEE and propose three simple principles to follow: organize your work, code for others and automate as much as you can. The first principle, "organize your work", deals with the overall organization of files and the documentation of a research workflow. "Code for others" emphasizes that we should take care in how we write code that has to be read by others or later by our future self. Finally, "automate as much as you can" is a proposal to avoid any manual treatment and to automate most, if not all, of the steps used in a research process to reduce errors and increase reproducibility. As software is not always the problem and will never be the solution, we illustrate these principles with good habits and tools, with a particular focus on their implementation in most popular software and languages in applied economics
How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics
Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of âreproducible researchâ in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics
How to make a pie? Reproducible Research for Empirical Economics & Econometrics
Les recherches empiriques en sciences humaines et sociales nĂ©cessitent la manipulation de nombreux fichiers : diffĂ©rents jeux de donnĂ©es, de multiples programmes, qu'ils soient destinĂ©s Ă la manipulation de donnĂ©es, aux traitements statistiques, aux estimations Ă©conomĂ©triques ou Ă des simulations, et de nombreux fichiers successifs de rĂ©sultats.Maitriser lâensemble des Ă©tapes du projet de recherche est indispensable si lâon souhaite pouvoir reproduire ou rĂ©pliquer les rĂ©sultats Ă long terme. Cette rigueur (fiabilitĂ©, traçabilitĂ©, reproductibilitĂ©), est dĂ©sormais de plus en plus exigĂ©e par notre profession ainsi que par les Ă©diteurs de revues scientifiques.AprĂšs avoir mis en Ă©vidence les enjeux de cette « recherche reproductible » (Claerbout, 1990) pour les Ă©conomistes, nous en dĂ©clinons les principes autour de trois axes simples : organiser les Ă©tapes du projet et les fichiers, Ă©crire des programmes clairs et documentĂ©s, et automatiser le plus possible les opĂ©rations jusquâau document prĂ©sentant les rĂ©sultats. Ces principes sont alors illustrĂ©s par diffĂ©rentes bonnes pratiques, en allant des plus simples au plus sophistiquĂ©es, avec un focus particulier sur les fonctionnalitĂ©s des logiciels les plus courants en Ă©conomie (Stata, R, SAS, Matlab, Mathematica, Gams)
How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics
TSE Working Paper, n. 18-933, July 2018Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of âreproducible researchâ in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics
Montréal-Moscou : Trois artistes, trois femmes, trois vagues, trois générations : Marina Popova, Luba Genush, Natasha Wrangel
Five authors present three artists of slavonic origin based in Québec, stressing their experience in exile and the affiliation of their paintings with soviet avant gardes. Statements by the artists. Most texts are published in their original version only. Biographical notes
How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics
Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of âreproducible researchâ in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics
How to make a pie? Reproducible Research for Empirical Economics & Econometrics
Les recherches empiriques en sciences humaines et sociales nĂ©cessitent la manipulation de nombreux fichiers : diffĂ©rents jeux de donnĂ©es, de multiples programmes, qu'ils soient destinĂ©s Ă la manipulation de donnĂ©es, aux traitements statistiques, aux estimations Ă©conomĂ©triques ou Ă des simulations, et de nombreux fichiers successifs de rĂ©sultats.Maitriser lâensemble des Ă©tapes du projet de recherche est indispensable si lâon souhaite pouvoir reproduire ou rĂ©pliquer les rĂ©sultats Ă long terme. Cette rigueur (fiabilitĂ©, traçabilitĂ©, reproductibilitĂ©), est dĂ©sormais de plus en plus exigĂ©e par notre profession ainsi que par les Ă©diteurs de revues scientifiques.AprĂšs avoir mis en Ă©vidence les enjeux de cette « recherche reproductible » (Claerbout, 1990) pour les Ă©conomistes, nous en dĂ©clinons les principes autour de trois axes simples : organiser les Ă©tapes du projet et les fichiers, Ă©crire des programmes clairs et documentĂ©s, et automatiser le plus possible les opĂ©rations jusquâau document prĂ©sentant les rĂ©sultats. Ces principes sont alors illustrĂ©s par diffĂ©rentes bonnes pratiques, en allant des plus simples au plus sophistiquĂ©es, avec un focus particulier sur les fonctionnalitĂ©s des logiciels les plus courants en Ă©conomie (Stata, R, SAS, Matlab, Mathematica, Gams)