Gata2 is a tissue-specific post-mitotic selector gene for midbrain GABAergic neurons

Midbrain GABAergic neurons control several aspects of behavior, but regulation of their development and diversity is poorly understood. Here, we further refine the midbrain regions active in GABAergic neurogenesis and show their correlation with the expression of the transcription factor Gata2. Using tissue-specific inactivation and ectopic expression, we show that Gata2 regulates GABAergic neuron development in the mouse midbrain, but not in rhombomere 1, where it is needed in the serotonergic lineage. Without Gata2, all the precursors in the embryonic midbrain fail to activate GABAergic neuron-specific gene expression and instead switch to a glutamatergic phenotype. Surprisingly, this fate switch is also observed throughout the neonatal midbrain, except for the GABAergic neurons located in the ventral dopaminergic nuclei, suggesting a distinct developmental pathway for these neurons. These studies identify Gata2 as an essential post-mitotic selector gene of the GABAergic neurotransmitter identity and demonstrate developmental heterogeneity of GABAergic neurons in the midbrain.Midbrain GABAergic neurons control several aspects of behavior, but regulation of their development and diversity is poorly understood. Here, we further refine the midbrain regions active in GABAergic neurogenesis and show their correlation with the expression of the transcription factor Gata2. Using tissue-specific inactivation and ectopic expression, we show that Gata2 regulates GABAergic neuron development in the mouse midbrain, but not in rhombomere 1, where it is needed in the serotonergic lineage. Without Gata2, all the precursors in the embryonic midbrain fail to activate GABAergic neuron-specific gene expression and instead switch to a glutamatergic phenotype. Surprisingly, this fate switch is also observed throughout the neonatal midbrain, except for the GABAergic neurons located in the ventral dopaminergic nuclei, suggesting a distinct developmental pathway for these neurons. These studies identify Gata2 as an essential post-mitotic selector gene of the GABAergic neurotransmitter identity and demonstrate developmental heterogeneity of GABAergic neurons in the midbrain.Midbrain GABAergic neurons control several aspects of behavior, but regulation of their development and diversity is poorly understood. Here, we further refine the midbrain regions active in GABAergic neurogenesis and show their correlation with the expression of the transcription factor Gata2. Using tissue-specific inactivation and ectopic expression, we show that Gata2 regulates GABAergic neuron development in the mouse midbrain, but not in rhombomere 1, where it is needed in the serotonergic lineage. Without Gata2, all the precursors in the embryonic midbrain fail to activate GABAergic neuron-specific gene expression and instead switch to a glutamatergic phenotype. Surprisingly, this fate switch is also observed throughout the neonatal midbrain, except for the GABAergic neurons located in the ventral dopaminergic nuclei, suggesting a distinct developmental pathway for these neurons. These studies identify Gata2 as an essential post-mitotic selector gene of the GABAergic neurotransmitter identity and demonstrate developmental heterogeneity of GABAergic neurons in the midbrain.Peer reviewe

Fundamental limits to learning closed-form mathematical models from data

Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection

A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber

Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.Peer reviewe

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits

Modeling of chlorinated solvents transport and natural attenuation in groundwater

International audienceThe objective of this study is to test differents modeling approaches and the reliability of the codes used in water risk assessment for a site contaminated with chlorinated solvents. The final goal is to elaborate, within the framework of the TRANSPOL research program, a guideline that could be used as a scientist support for contaminated aquifers management. The models performed by 4 teams (ANTEA, ENSMP, ENVIROS and INERIS) to simulate a contamination of groundwater by perchloroethene were compared. The total amount of perchloroethene discharged into the aquifer was unknown and a simplified conceptual model was considered. A first synthesis of the results obtained shows two principal difficulties : evaluate source concentration and simulate natural attenuation phenomenon (sorption/degradation). More accurate diagnosis is needed in order to reduce the uncertainties of model parameters

Aparato para el diagnóstico y monitorización de la esteatosis hepática basado en la medición de la impedancia eléctrica

Aparato para el diagnóstico y monitorización de la esteatosis hepática basado en la medición de la impedancia eléctrica. La presente invención hace referencia a un sistema para determinar de forma rápida el grado de esteatosis hepática (grasa en el hígado) a través de una medida directa de la impedancia eléctrica hepática a una o más frecuencias. La medida se realiza mediante sensores de superficie o mínimamente invasivos, pudiéndose acoplar estos a otros dispositivos de uso médico (e.g. sondas laparoscópicas). Mediante un algoritmo de interpolación basado en correlaciones entre la impedancia y el porcentaje de grasa hepática en biopsias de referencia, el sistema es capaz de determinar el grado de esteatosis hepática en el órgano medido de forma inmediata y sin necesidad de otro tipo de intervención. Esto permite su aplicación a procedimientos como el trasplante hepático, posibilitando un diagnóstico rápido de viabilidad, así como otros procedimientos quirúrgicos y sobre órganos explantados.Peer reviewedConsejo Superior de Investigaciones Científicas (España)B1 Patente con informe sobre el estado de la ténic

Modeling of chlorinated solvents transport and natural attenuation in groundwater

International audienceThe aim of this study is to develop a natural attenuation model to predict the fate and transport of chlorinated solvents and their degradation products in saturated groundwater systems