Characterising The Atmospheric Dynamics Of HD209458b-like Hot Jupiters Using AI Driven Image Recognition/Categorisation

In-order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately this increases both the computational cost and output size of said models. We intend to explore if AI-image-recognition can alleviate this burden. We used DYNAMICO to run a series of HD209458-like models with different orbital-radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorisation convolutional-neural-networks (CNN), which we applied to our outer-atmosphere-equilibrated models. The features detected by our CNNs revealed that our models fall into two regimes: models with a shorter orbital-radii exhibit significant global mixing which shapes the entire atmospheres dynamics. Whereas, models with longer orbital-radii exhibit negligible mixing except at mid-pressures. Here, the initial non-detection of any trained features revealed a surprise: a night-side hot-spot. Analysis suggests that this occurs when rotational influence is sufficiently weak that divergent flows from the day-side to the night-side dominate over rotational-driven transport, such as the equatorial jet. We suggest that image-classification may play an important role in future, computational, atmospheric studies. However special care must be paid to the data feed into the model, from the colourmap, to training the CNN on features with enough breadth and complexity that the CNN can learn to detect them. However, by using preliminary-studies and prior-models, this should be more than achievable for future exascale calculations, allowing for a significant reduction in future workloads and computational resources.Comment: Accepted for publication in Ap

Forced expression of Lmx1b enhances differentiation of mouse ES cells into serotonergic neurons

The LIM homeodomain transcription factor Lmx1b is a key factor in the specification of the serotonergic neurotransmitter phenotype. Here, we explored the capacity of Lmx1b to direct differentiation of mouse embryonic stem (mES) cells into serotonergic neurons. mES cells stably expressing human Lmx1b were generated by lentiviral vector infection. Clones expressing Lmx1b at a low level showed increased neurogenesis and elevated production of neurons expressing serotonin, serotonin transporter, Tryptophan hydroxylase 2, and transcription factor Pet1, the landmarks of serotonergic differentiation. To explore the role of Lmx1b in the specification of the serotonin neurotransmission phenotype further, a conditional system making use of a floxed inducible vector targeted into the ROSA26 locus and a hormone-dependent Cre recombinase was engineered. This novel strategy was tested with the reporter gene encoding human placental alkaline phosphatase, and demonstrated its capacity to drive transgene expression in nestin+ neural progenitors and in Tuj1+ neurons. When it was applied to the inducible expression of human Lmx1b, it resulted in elevated expression of serotonergic markers. Treatment of neural precursors with the floor plate signal Sonic hedgehog further enhanced differentiation of Lmx1b-overexpressing neural progenitors into neurons expressing 5-HT, serotonin transporter, Tryptophan hydroxylase 2 and Pet1, when compared to Lmx1b-non expressing progenitors. Together, our results demonstrate the capacity of Lmx1b to specify a serotonin neurotransmitter phenotype when overexpressed in mESC-derived neural progenitors

Functional and Biochemical Characterization of Hepatitis C Virus (HCV) Particles Produced in a Humanized Liver Mouse Model

Lipoprotein components are crucial factors for hepatitis C virus (HCV) assembly and entry. As hepatoma cells producing cell culture-derived HCV (HCVcc) particles are impaired in some aspects of lipoprotein metabolism, it is of upmost interest to biochemically and functionally characterize the in vivo produced viral particles, particularly regarding how lipoprotein components modulate HCV entry by lipid transfer receptors such as scavenger receptor BI (SR-BI). Sera from HCVcc-infected liver humanized FRG mice were separated by density gradients. Viral subpopulations, termed HCVfrg particles, were characterized for their physical properties, apolipoprotein association, and infectivity. We demonstrate that, in contrast to the widely spread distribution of apolipoproteins across the different HCVcc subpopulations, the most infectious HCVfrg particles are highly enriched in apoE, suggesting that such apolipoprotein enrichment plays a role for entry of in vivo derived infectious particles likely via usage of apolipoprotein receptors. Consistent with this salient feature, we further reveal previously undefined functionalities of SR-BI in promoting entry of in vivo produced HCV. First, unlike HCVcc, SR-BI is a particularly limiting factor for entry of HCVfrg subpopulations of very low density. Second, HCVfrg entry involves SR-BI lipid transfer activity but not its capacity to bind to the viral glycoprotein E2. In conclusion, we demonstrate that composition and biophysical properties of the different subpopulations of in vivo produced HCVfrg particles modulate their levels of infectivity and receptor usage, hereby featuring divergences with in vitro produced HCVcc particles and highlighting the powerfulness of this in vivo model for the functional study of the interplay between HCV and liver components

Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5

We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes

Impact of CO2 and climate on the Last Glacial Maximum vegetation: results from the ORCHIDEE/IPSL models

International audienceVegetation reconstructions from pollen data for the Last Glacial Maximum (LGM), 21 ky ago, reveal lanscapes radically different from the modern ones, with, in particular, a massive regression of forested areas in both hemispheres. Two main factors have to be taken into account to explain these changes in comparison to today's potential vegetation: a generally cooler and drier climate and a lower level of atmospheric CO2. In order to assess the relative impact of climate and atmospheric CO2 changes on the global vegetation, we simulate the potential modern vegetation and the glacial vegetation with the dynamical global vegetation model ORCHIDEE, driven by outputs from the IPSL_CM4_v1 atmosphere-ocean general circulation model, under modern or glacial CO2 levels for photosynthesis. ORCHIDEE correctly reproduces the broad features of the glacial vegetation. Our modelling results support the view that the physiological effect of glacial CO2 is a key factor to explain vegetation changes during glacial times. In our simulations, the low atmospheric CO2 is the only driver of the tropical forests regression, and explains half of the response of temperate and boreal forests to glacial conditions. Our study shows that the sensitivity to CO2 changes depends on the background climate over a region, and also depends on the vegetation type, needleleaf trees being much more sensitive than broadleaf trees in our model. This difference of sensitivity leads to a dominance of broadleaf types in the remaining simulated forests, which is not supported by pollen data, but nonetheless suggests a potential impact of CO2 on the glacial vegetation assemblages. It also modifies the competitivity between the trees and makes the amplitude of the response to CO2 dependent on the initial vegetation state

Characterizing the Atmospheric Dynamics of HD 209458b-like Hot Jupiters Using AI-driven Image Recognition/Categorization

In order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately, this increases both the computational cost and output size of said models. We intend to explore if AI image recognition can alleviate this burden. We used DYNAMICO to run a series of HD 209458-like models with different orbital radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorization convolutional neural networks (CNNs), which we applied to our outer-atmosphere-equilibrated models. The features detected by our CNNs revealed that our models fall into two regimes: models with shorter orbital radii exhibit significant global mixing that shapes the dynamics of the entire atmosphere, whereas models with longer orbital-radii exhibit negligible mixing except at mid-pressures. Here the initial nondetection of any trained features revealed a surprise: a nightside hot spot. Analysis suggests that this occurs when rotational influence is sufficiently weak that divergent flows from the dayside to the nightside dominate over rotational-driven transport, such as the equatorial jet. We suggest that image classification may play an important role in future, computational, atmospheric studies. However special care must be paid to the data feed into the model, from the color map, to training the CNN on features with enough breadth and complexity that the CNN can learn to detect them. However, by using preliminary studies and prior models, this should be more than achievable for future exascale calculations, allowing for a significant reduction in future workloads and computational resources

Corrigendum to "Impact of CO₂ and climate on the Last Glacial Maximum vegetation: results from the ORCHIDEE/IPSL models" published in Clim. Past, 7, 557–577, 2011

No abstract available

Characterising The Atmospheric Dynamics Of HD209458b-like Hot Jupiters Using AI Driven Image Recognition/Categorisation

Accepted for publication in ApJInternational audienceIn-order to understand the results of recent observations of exoplanets, models have become increasingly complex. Unfortunately this increases both the computational cost and output size of said models. We intend to explore if AI-image-recognition can alleviate this burden. We used DYNAMICO to run a series of HD209458-like models with different orbital-radii. Training data for a number of features of interest was selected from the initial outputs of these models. This was used to train a pair of multi-categorisation convolutional-neural-networks (CNN), which we applied to our outer-atmosphere-equilibrated models. The features detected by our CNNs revealed that our models fall into two regimes: models with a shorter orbital-radii exhibit significant global mixing which shapes the entire atmospheres dynamics. Whereas, models with longer orbital-radii exhibit negligible mixing except at mid-pressures. Here, the initial non-detection of any trained features revealed a surprise: a night-side hot-spot. Analysis suggests that this occurs when rotational influence is sufficiently weak that divergent flows from the day-side to the night-side dominate over rotational-driven transport, such as the equatorial jet. We suggest that image-classification may play an important role in future, computational, atmospheric studies. However special care must be paid to the data feed into the model, from the colourmap, to training the CNN on features with enough breadth and complexity that the CNN can learn to detect them. However, by using preliminary-studies and prior-models, this should be more than achievable for future exascale calculations, allowing for a significant reduction in future workloads and computational resources

Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

Atmospheric CO<sub>2</sub> drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO<sub>2</sub> in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. <br><br> We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them