Influence of insulators on transgene expression from integrating and non-integrating lentiviral vectors.

International audienceBACKGROUND: The efficacy and biosafety of lentiviral gene transfer is influenced by the design of the vector. To this end, properties of lentiviral vectors can be modified by using cis-acting elements such as the modification of the U3 region of the LTR, the incorporation of the central flap (cPPT-CTS) element, or post-transcriptional regulatory elements such as the woodchuck post-transcriptional regulatory element (WPRE). Recently, several studies evaluated the influence of the incorporation of insulators into the integrating lentiviral vector genome on transgene expression level and position effects. METHODS: In the present study, the influence of the matrix attachment region (MAR) of the mouse immunoglobulin-κ (Ig-κ) or the chicken lysozyme (ChL) gene was studied on three types of HIV-1-derived lentiviral vectors: self-inactivating (SIN) lentiviral vectors (LV), double-copy lentiviral vectors (DC) and non-integrating lentiviral vectors (NILVs) in different cell types: HeLa, HEK293T, NIH-3T3, Raji, and T Jurkat cell lines and primary neural progenitors. RESULTS AND DISCUSSION: Our results demonstrate that the Ig-κ MAR in the context of LV slightly increases transduction efficiency only in Hela, NIH-3T3 and Jurkat cells. In the context of double-copy lentiviral vectors, the Ig-κ MAR has no effect or even negatively influences transduction efficiency. In the same way, in the context of non-integrating lentiviral vectors, the Ig-κ MAR has no effect or even negatively influences transduction efficiency, except in differentiated primary neural progenitor cells.The ChL MAR in the context of integrating and non-integrating lentiviral vectors shows no effect or a decrease of transgene expression in all tested conditions. CONCLUSIONS: This study demonstrates that MAR sequences not necessarily increase transgene expression and that the effect of these sequences is probably context dependent and/or vector dependent. Thus, this study highlights the importance to consider a MAR sequence in a given context. Moreover, other recent reports pointed out the potential effects of random integration of insulators on the expression level of endogenous genes. Taken together, these results show that the use of an insulator in a vector for gene therapy must be well assessed in the particular therapeutic context that it will be used for, and must be balanced with its potential genotoxic effects

Traduction et Adaptation culturelle du questionnaire de satisfaction SADL (Satisfaction with amplification in daily life)

National audienc

Validation of TROPOMI Surface UV Radiation Product

The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite was launched on 13 October 2017 to provide the atmospheric composition for atmosphere and climate research. The S5P is a sun-synchronous polar-orbiting satellite providing global daily coverage. The TROPOMI swath is 2600 km wide, and the ground resolution for most data products is 7.2x3.5 km2 (5.6x3.5 km2 since 6 August 2019) at nadir. The Finnish Meteorological Institute (FMI) is responsible for the development and processing of the TROPOMI Surface Ultraviolet (UV) Radiation Product which includes 36 UV parameters in total. Ground-based data from 25 sites located in arctic, subarctic, temperate, equatorial and antarctic areas were used for validation of TROPOMI overpass irradiance at 305, 310, 324 and 380 nm, overpass erythemally weighted dose rate / UV index and erythemally weighted daily dose for the period from 1 January 2018 to 31 August 2019. The validation results showed that for most sites 60–80% of TROPOMI data was within ±20% from ground-based data for snow free surface conditions. The median relative differences to ground-based measurements of TROPOMI snow free surface daily doses were within ±10% and ±5% at two thirds and at half of the sites, respectively. At several sites more than 90% of clear sky TROPOMI data were within ±20% from ground-based measurements. Generally median relative differences between TROPOMI data and ground-based measurements were a little biased towards negative values, but at high latitudes where nonhomogeneous topography and albedo/snow conditions occurred, the negative bias was exceptionally high, from -30% to -65%. Positive biases of 10–15% were also found for mountainous sites due to challenging topography. The TROPOMI Surface UV Radiation Product includes quality flags to detect increased uncertainties in the data due to heterogeneous surface albedo and rough terrain which can be used to filter the data retrieved under challenging conditions

La Chine à l’heure des villes intelligentes

International audienc

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Perspectives asiatiques sur les Smart Cities

International audienc

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Experimental study of time series forecasting methods for groundwater level prediction

International audienceGroundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data

Effects of biomass, age and functional traits on regrowth of arable weeds after cutting

Conference: 24th German Conference on Weed Biology and Weed ControlLocation: Stuttgart Hohenheim, GERMANYDate: MAR 04-06, 2008International audienceIn sown field margin stripes or pluriannual forage crops, arable weeds are exposed to high competition and regular mowing or hay cutting, to which they may react very differently. A greenhouse experiment permitted to understand some key factors shaping the ability of common arable weeds and forage crops to grow after such cuttings. Even without competition, cutting shoots at 5cm height reduced biomass production of all 10 species studied, but 6 annual broadleaf weeds were much more affected than 2 perennial forage crops (Dactylis, Medicago) and 2 annual grasses (Alopecurus, Bromus), confirming our hypothesis. Variation of regrowth speed within each species was always positively related to the plant size before cutting, suggesting that bigger plants can remobilize more belowground resources. But this is only true for plants sown at the same date, as older weeds showed reduced regrowth despite their bigger size. Carbohydrate resources of older plants might have already been depleted for reproductive growth. This basic knowledge may be used to construct weed demography models and to develop innovative cropping systems. If most annual weeds cannot grow and reproduce when cut under real conditions with competition, introducing mown temporary grasslands into crop rotations may readily be used as an element of Integrated Weed Management