Bmi1 Is Down-Regulated in the Aging Brain and Displays Antioxidant and Protective Activities in Neurons

Aging increases the risk to develop several neurodegenerative diseases, although the underlying mechanisms are poorly understood. Inactivation of the Polycomb group gene Bmi1 in mice results in growth retardation, cerebellar degeneration, and development of a premature aging-like phenotype. This progeroid phenotype is characterized by formation of lens cataracts, apoptosis of cortical neurons, and increase of reactive oxygen species (ROS) concentrations, owing to p53-mediated repression of antioxidant response (AOR) genes. Herein we report that Bmi1 expression progressively declines in the neurons of aging mouse and human brains. In old brains, p53 accumulates at the promoter of AOR genes, correlating with a repressed chromatin state, down-regulation of AOR genes, and increased oxidative damages to lipids and DNA. Comparative gene expression analysis further revealed that aging brains display an up-regulation of the senescence-associated genes IL-6, p19Arf and p16Ink4a, along with the pro-apoptotic gene Noxa, as seen in Bmi1-null mice. Increasing Bmi1 expression in cortical neurons conferred robust protection against DNA damage-induced cell death or mitochondrial poisoning, and resulted in suppression of ROS through activation of AOR genes. These observations unveil that Bmi1 genetic deficiency recapitulates aspects of physiological brain aging and that Bmi1 over-expression is a potential therapeutic modality against neurodegeneration

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Altimetry for the future: building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Intégration du contexte par réseaux bayésiens pour la détection et le suivi multi-cibles

The general framework of this work concerns the driver assistance systems and more especially security. The objective here is to monitor the vehicle environment using a telemeter and inform the driver of potential hazardous situations. Maneuvers of collision avoidance or mitigation can then be considered. Two particular points have focused our attention: the object detection since it directly determines the overall performance of the method, and the association-tracking process which associates the available measurements to each tracked object. The telemetric data must be processed, through a detection stage that aggregates the measurement from the same object, in order to estimate the number of objects in the scene and their distance to the sensor. We propose in this thesis a method for object detection that uses not only the available measurements but also the geometric characteristics related to the applicative context. For the association stage, we particularly focus on the probabilistic data association methods that consider the available measure may not be linked to an object. These methods rely on the concept of detection and false alarm probabilities. These probabilities, and more particularly the probability of detection, are not only strongly linked to the detector, but also to the context of the scene (sensor / object and context object / object). To integrate the contextual information, we propose an association-tracking method based on the Bayesian networks. It allows an integration of the parameters related to the characteristics of the objects and the sensor in the determination of the detection probabilityCes travaux se placent dans le cadre général de l'assistance au conducteur et plus particulièrement de la sécurité. L'objectif est ici de surveiller l'environnement d'un véhicule grâce à un capteur télémétrique à balayage et d'informer le conducteur de situations potentiellement dangereuses. Ce dispositif permet alors d'envisager une manoeuvre d'évitement ou d'atténuation de collision. Deux points particuliers ont retenu notre attention : la détection d'objets qui occupe une place privilégiée car elle conditionne directement les performances globales de la méthode, et le processus d'association/suivi qui doit permettre d'associer efficacement les mesures disponibles à chaque objet suivi. Les données télémétriques utilisées nécessitent de passer par une étape de détection afin d'estimer le nombre d'objets présents dans la scène et leur distance au capteur, en procédant à une agrégation des mesures liées au même objet. Nous proposons en particulier dans ce mémoire une méthode de détection d'objets qui exploite non seulement la nature des mesures disponibles mais également les caractéristiques géométriques particulières liées au contexte applicatif. L'approche retenue pour l'étape d'association repose sur les méthodes d'association probabiliste de données qui permettent notamment de considérer le fait qu'une mesure disponible puisse ne pas être liée à un objet, en exploitant donc directement les notions de probabilité de détection et de fausse alarme. Ces probabilités, et notamment la probabilité de détection, demeurent non seulement fortement liées au détecteur, mais également au contexte de la scène : contexte capteur/objet et contexte objet/objet. Pour pouvoir intégrer ces informations globales de contexte, nous proposons une méthode d'association-suivi basée sur les réseaux bayésiens qui autorise l'intégration de paramètres liés aux caractéristiques des objets et du capteur dans la détermination de la probabilité de détection

Turning Lakes Into River Gauges Using the LakeFlow Algorithm

Abstract Rivers and lakes are intrinsically connected waterbodies yet they are rarely used to hydrologically constrain one another with remote sensing. Here we begin to bridge the gap between river and lake hydrology with the introduction of the LakeFlow algorithm. LakeFlow uses river‐lake mass conservation and observations from the Surface Water and Ocean Topography (SWOT) satellite to provide river discharge estimates of lake and reservoir inflows and outflows. We test LakeFlow performance at three lakes using a synthetic SWOT data set assuming the maximum measurement errors defined by the mission science requirements, and we include modeled lateral inflow and lake evaporation data to further constrain the mass balance. We find that LakeFlow produces promising discharge estimates (median Nash‐Sutcliffe efficiency = 0.88, relative bias = 14%). LakeFlow can inform water resources management by providing global lake inflow and outflow estimates, highlighting a path for recognizing rivers and lakes as an interconnected system

Effect of Recombinant alpha1-Antitrypsin Fc-Fused (AAT-Fc)Protein on the Inhibition of Inflammatory Cytokine Production and Streptozotocin-Induced Diabetes

Contains fulltext : 118341.pdf (publisher's version ) (Open Access)alpha1-Antitrypsin (AAT) is a member of the serine proteinase inhibitor family that impedes the enzymatic activity of serine proteinases, including human neutrophil elastase, cathepsin G and neutrophil proteinase 3. Here, we expressed recombinant AAT by fusing the intact AAT gene to the constant region of IgG1 to generate soluble recombinant AAT-Fc protein. The recombinant AAT-Fc protein was produced in Chinese hamster ovary (CHO) cells and purified using mini-protein A affinity chromatography. Recombinant AAT-Fc protein was tested for antiinflammatory function and AAT-Fc sufficiently suppressed tumor necrosis factor (TNF)-alpha-induced interleukin (IL)-6 in human peripheral blood mononuclear cells (PBMCs) and inhibited cytokine-induced TNFalpha by different cytokines in mouse macrophage Raw 264.7 cells. However, AAT-Fc failed to suppress lipopolysaccharide-induced cytokine production in both PBMCs and macrophages. In addition, our data showed that AAT-Fc blocks the development of hyperglycemia in a streptozotocin-induced mouse model of diabetes. Interestingly, we also found that plasma-derived AAT specifically inhibited the enzymatic activity of elastase but that AAT-Fc had no inhibitory effect on elastase activity