The methodology applied for selecting channels is the one suggested by [START_REF] Rodgers | Information contentand opyimisation of high spectral resolution measurements[END_REF] and proved to be a good a priori method for the determination of an optimal channel set by [START_REF] Rabier | Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances[END_REF]. This method relies on evaluating the impact of the addition of one channel at time on a scalar figure of merit reflecting the improvement of the analysis error over the background one. The figure of merit we chose to iterate the selection process is the Degrees of Freedom for Signal (DFS) computed as follows: DFS = 𝑇𝑟 𝐈 -𝐈 + 𝐁 𝐇 𝐓 𝐑 -𝟏 𝐇 -1 , with H the Jacobian matrix, I the identity matrix and B background-error covariance matrix.

We performed a channel selection on each of the 77 profiles chosen for this study, stopping the iterative process at the threshold of 500 since currently this is the number distributed of IASI channels to NWP centres. An average of the DFS evolution is shown in Figure 4. From these 77 independent selections we chose the 500 channels most frequently selected (channels chosen in 37.7% or more of the evaluated cases). The selection consists of 300 channels belonging to band 1 (277 temperature and 23 surface-sensitive channels) and 200 from the water vapour band ) [Figure 5].

This selection, plus other groups of channels retained more or less frequently, have been tested on a larger set of 6267 atmospheric profiles, have been tested on a larger set of 6267 atmospheric profiles. The results obtained show an improvement in the Retrievals compared to the Background profiles when using the selected channels (Figure 6). Regarding temperature, the improvement is especially pronounced in the very low layers (from 960 to 990 hPa approximately) and in the upper troposphere, with improvements reaching up to 30% at 200 hPa. For water vapour, on the other hand, this improvement is more pronounced in the medium/high layers (between 600 and 200 hPa) with improvements up to 50%.

𝑏𝑙𝑎𝑎𝑎𝑎𝑎

I. Introduction

As the EUMETSAT Polar System-Second Generation (EPS-SG) is being prepared, a new generation of the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) has being designed. The IASI New Generation (IASI-NG), which is expected to be launched in 2022 on board the Metop second generation series, will measure at 16921 wavelengths in each sounding pixel with a spectral sampling of 0.125 cm -1 and a signal-to-noise ratio improved by a factor 2 compared to its predecessor. Measurement precision will be improved as well. The IASI-NG characteristics will lead to huge improvements in detection and retrieval of numerous chemical species and aerosols, and in thermodynamic profiles retrievals, as well as providing, like its predecessor, a huge contribution to Numerical Weather Prediction (NWP).

The high amount of data resulting from IASI-NG will present many challenges in the areas of data transmission, storage and assimilation. Moreover, the number of individual pieces of information will be not exploitable in an operational NWP context and the choice of an optimal data subset will be needed. For all these reasons, an appropriate IASI-NG channel selection is going to be performed aiming to select the most informative channels for NWP.

An Infrared Atmospheric Sounding Interferometer -New Generation (IASI-NG) channel selection for Numerical Weather Prediction

Francesca Vittorioso, Vincent Guidard, Nadia Fourrié CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France francesca.vittorioso@meteo.fr IASI-NG is a space-borne FTS, based on the concept of the Mertz interferometer, that will measure IR radiation emitted from the Earth within the range 645 and 2760 cm -1 (3.6 -15.5 μm). It will benefit of a spectral sampling of 0.125 cm -1 , a spectral resolution of 0.25 cm -1 after apodization and a signal-tonoise ratio improved by a factor 2 compared to its predecessor [START_REF] Crevoisier | Towards IASI-New Generation (IASI-NG) : impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables[END_REF]]. Measurement precision will be improved starting from the 1 K in temperature and 10% in humidity IASI precision.

II. IASI-NG context

The IASI-NG spectrum will be split into four bands, compared to three bands for IASI. For this study, we focused on bands 1 and 2. We selected the first 2448 contiguous channels of band 1 (from 645.000 to 950.875 cm -1 ) and the last 3601 contiguous channels of band 2 (from channel 6841 up to 10 441, namely from 1500.000 to 1950.000 cm -1 ). The total number of channels considered is 6049. This choice excludes from the study the ozone-related part of the band 1, since the minimisation of this quantity is not performed in the operational assimilation at Météo-France yet. On the other hand, the second half part of band 2 has been preferred to the first half since it will provide good information about water vapor, without being affected by the influence of trace gases (e.g. N2O or CH4). Moreover, this spectral area will be consistent with the one that the Infrared Sounder (IRS), which will fly on board the Meteosat Third Generation (MTG), will provide in the next few years.

The areas just described are highlighted on the spectrum of Figure 1.

III. Case study

Since the actual data from IASI-NG will only be available in a few years, we exploited the database from Andrey-Andrés et al. (2018) containing 5242047 simulated observations using the scan geometry of IASI for both IASI and IASI-NG and covering four dates in the middle of each season from 2013 in order to provide a maximum of meteorological variability.

For the purpose of this project we selected subsets of data to be used for the various steps of this study.

For the observation error estimation step, a subset of 6267 profiles has been selected matching the following criteria: Instrument Zenith Angle (IZA) < 2.8 (i.e. at nadir), over sea (water) clear sky day/night illumination conditions.

For the channel selection stage, on the other hand, an agglomerative hierarchical clustering technique has been used in order to isolate in the previous 6267 subset, a smaller group of 77 cases (both typical and extreme).

IV. Observation error estimation

For the present study we decided to use a full covariance matrix of the observation errors.

(a) (b)

The selection proposed in this paper consists of the 500 channels most frequently selected by using the optimal selection method.

The selected channels depend on many components, such as the atmospheric profiles, the errors prescribed to the background and the observations. In further studies, it could be interesting to compare several methods using the same inputs (B, R, observations) and to assess the impact on the metrics we used, such as the DFS statistics and the rate of improvement. An intercomparison exercise using several B matrices provided by different NWP centres in the selection process may be of interest to evaluate the overall sensitivity of the selection to the background errors.

Future works may focus on spectral areas not yet explored in this study. Scanning and evaluating channels sensitive to other chemical species would make the selection even more complete and optimal. Cloudy and over land case studies could equally be analysed. Moreover, it would be interesting to evaluate the IASI-NG selection in a more realistic context as the one of a global model such as the Météo-France Action de Recherche petite Echelle Grande Echelle (ARPEGE).

In order to evaluate the optimality of the already well-known channel selection methodology applied along this work, and as a good sanity check, a completely random selection was performed. First of all, 77 different 500 random channel selections were produced without any constraints or conditions except that the aforementioned channels belonged to the spectral areas covered during this study.

After that, 1D-Var assimilation experiments have been performed by using a different random channel selection for each of the 77 case study profiles. The aim was to compare these results with those from the assimilation experiments carried out using for each of the 77 cases its optimal selection obtained through the DFS method.

The optimal selection and the proposed 500 channel selection perform better than random ones. The gain is particularly important for temperature in the upper part of the troposphere/stratosphere. In red is the average on the 77 case study profiles of the ROI obtained using for each of them the 500 channels most frequently selected through the DFS method. The green curve shows the results produced using the same random channel selection applied to all 77 profiles.

Paper under review at QJRMS: Vittorioso F., V. Guidard and N. Fourrie: An Infrared Atmospheric Sounding Interferometer -New Generation (IASI-NG) channel selection for Numerical Weather Prediction

Figure 1 :

 1 Figure 1: Example of simulated IASI-NG spectrum (divided into four spectral bands, which are here depicted together with the sensitivity of the different spectral regions). The blue boxes highlight the preselected areas to be examined during this study: the first 2448 consecutive channels of band 1 and the last 3601 consecutive channels of band 2 (from channel 6841 up to 10 441).

Figure 2 :

 2 Figure 2: Geographical distribution of the profile sets taken as case study. The top panels show the 6267 profiles preselected to be used for the observation error evaluation. They are displayed in different colors depending on the illumination conditions (a) and the day of the year (b). The bottom panels (c) and (d) refer to the 77 profiles picked up, starting from the 6267 subset, through a hierarchical agglomerative technique in order to carry out the proper selection phase.

  Figure 3: (a) Diagnosed IASI-NG error correlations from 1D-Var output. The boxes outlined by dashed lines highlight the results corresponding to the 2448 channels of band 1 (bottom left) and to the 3601 channels of band 2 (top right). The bottom right and top left boxes, on the other hand, refer to the diagnosed crossed correlations. (b) Diagnosed IASI-NG observation-error standard deviations from 1D-Var output (orange), standard deviations from 1D-Var output after reconditioning (dark blue), standard deviations of the innovations (light blue), instrument noise (red).

Figure 4 :

 4 Figure 4: DFS trend values in channel selection averaged on the 77 case study profiles. The solid blue line corresponds to the total DFS. The pink, green and orange ones, on the other hand, refer to the the skin temperature, humidity and temperature terms respectively. The shaded areas associated to each curve report the standard deviations associated to that specific term.

Figure 6 :

 6 Figure 6: Rate of Improvement (ROI) of analysis-error standard deviations compared to the background ones averaged on 6267 profiles, for temperature and humidity. It is a comparison among the results obtained for the groups of channels chosen at least once, in 20, 60, 100% of cases and the ROI obtained through our 500 channel selection. Negative values mean Retrievals improved compared to the Background.

Figure 5 :

 5 Figure5: Spectral location of the 500 selected channels: 300 in band 1 (magenta) and 200 in band 2 (green dots). The left panel represents the preselected band 1 area (from 645.000 to 950.875 cm -1 ), the bottom panel the band 2 one (from 1500.000 to 1950.000 cm -1 ).

Figure 8 :

 8 Figure 8: Histograms of ROI averaged on the 77 profiles and the vertical (total or partial) column, as a function of temperature, humidity and for the total. Blue bars refer to the optimal selection, the orange ones to a different random selection for each profile, the red ones to the 500 channel selection and the green bars to a single random selection applied over the whole 77 profile subset.

Figure 7 :

 7 Figure 7: ROI of Retrieval Standard Deviations compared to the Background ones, for temperature (left panel) and humidity (right).In red is the average on the 77 case study profiles of the ROI obtained using for each of them the 500 channels most frequently selected through the DFS method. The green curve shows the results produced using the same random channel selection applied to all 77 profiles.

Acknowledgements

This research has received funding from CNES in the framework of the IASI-NG project.


Pauline Bregigeon

Marie Frénéa-Robin

Laure Franqueville

Charlotte Rivière

Christian Vollaire

Julien Marchalot

HIGH THROUGHPUT ELECTROPORATION MICROSYSTEM USING SINE WAVE BURSTS TO DELIVER BIOMOLECULES INTO CELL SPHEROIDS

Keywords: Cell spheroids, Sine wave electroporation, Micro-structured hydrogel, Microsystem, Electrochemotherapy

Reversible electroporation (EPN) is a method for introducing molecules into cells without permanent damage based on the application of pulsed electric fields. The development of innovative in vitro assays exploiting 3D cell models such as spheroids can be of great help to assess the potential of cancer treatments based on EPN. The parallel treatment of tens of spheroids of similar characteristics (size, shape) is required to produce statistical data. To address this challenge, we designed a microfluidic platform enabling culture and electroporation of a large number of spheroids sharing similar characteristics without requiring any manipulation. Here, we demonstrate the delivery of an anti-cancer agent in spheroids using sine wave bursts.

INTRODUCTION

The clinical interest for electroporation-based therapies, including electrochemotherapy (ECT), has drastically increased in the last few years, and spheroids have been identified as a relevant model to study EPN in vitro, as they can mimic various normal and pathological situations. In comparison with cells grown in 2D on flat surfaces, multicellular spheroids reproduce more accurately the structure of a microtissue in which each cell interacts with its neighbors via the formation of junctions ensuring cellular cohesion and communication [START_REF] Zanoni | 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained[END_REF] . In the literature, the proposed approaches usually consist in first fabricating the spheroids using dropletbased microfluidics 2 or hanging drop methods [START_REF] Chopinet | First explanations for differences in electrotransfection efficiency in vitro and in vivo using spheroid model[END_REF] , and then introducing them in an electroporation cuvette connected to a pulse generator. Thus, it involves several handling steps, which can potentially damage spheroids. Moreover, it leads to their random distribution in the cuvette, which may induce differences in the electric field perceived from one spheroid to the other. There is therefore a strong need for the development of new tools providing easiness of use, high throughput and results reproducibility. Our approach intends to overcome those drawbacks as, to the best of our knowledge, there is no existing solution enabling culture of spheroids of controlled size and shape, easy introduction of fresh medium and subsequent electroporation inside a unique device.

EXPERIMENTAL

A reusable polydimethylsiloxane (PDMS) mold is first made from a micro-milled metal master mold with a replica molding process. A thin layer of 2% agarose hydrogel pre-heated at 80°C is poured onto it and an ITO coated glass slide functionnalized with (3-Aminopropyl) triethoxysilane (APTS) is placed over it before demolding (Figure 1a). Human colorectal cancer cells (HT29) are seeded in this porous microstructured hydrogel (320 wells of 200 µm diameter) [START_REF] Rivière | Plaque de micropuits en hydrogel biocompatible[END_REF] , and let grown for 3 days until they fill the microwells (Figure 1b). Another ITO coated glass electrode is then placed over the hydrogel to form the EPN microfluidic chamber (Figure 1c), with tubing allowing for the injection of EPN buffer (10 mM Hepes, 1 mM MgCl2(6H2O), 250 mM sucrose, pH=7,1, σ=300 µS/cm) supplemented with an anticancer drug (bleomycin, 20 µg/mL). The distance between both electrodes is 1 mm. Medium exchange in the hydrogel is monitored by impedance measurements.

EPN is performed by applying 2 sine wave bursts (10 kHz, 5 ms) [START_REF] Chopinet | First explanations for differences in electrotransfection efficiency in vitro and in vivo using spheroid model[END_REF][START_REF] García-Sánchez | Sine wave electropermeabilization reveals the frequency-dependent response of the biological membranes[END_REF] . The relevance of such waveform for small molecule delivery into cells has recently been discussed [START_REF] García-Sánchez | Sine wave electropermeabilization reveals the frequency-dependent response of the biological membranes[END_REF] . The electric field effectively perceived by the spheroids (around 800 V/cm) is determined by estimating the electrical losses, from impedance measurements fitted to a theoretical model (data not shown). Electric field distribution in the microfluidic chamber is also determined, thanks to COMSOL modelling. To evaluate cellular viability 3 days after EPN, cells are marked with two fluorescent agents: fluorescein diacetate (FDA), a marker of living cells, and propidium iodide (PI), indicator of loss of membrane integrity due to cell death.

RESULTS AND DISCUSSION

With the COMSOL module "Electric Currents in Layered Shells", the device can be modelled as two ITO conductive layers of measured conductivity 7.10 5 S/m and thickness 134 nm, sandwiching a block representing the microfluidic chamber filled with EPN medium. When voltage is applied between both electrodes (Figure 2a), the resulting electric field in the chamber containing hydrogel and spheroids is homogeneous (Figure 2b), with less than 1% variation in intensity over the whole chamber, ensuring that all the spheroids are submitted to the same electric field. Prior to electroporation, the conductive culture medium is replaced by low conductivity EPN buffer solution which is slowly injected in the microfluidic chamber. Effective medium exchange is confirmed using impedance measurements. Results show that a 2.5 mL injection in 10 min is necessary to reach a plateau (Figure 2c), meaning that the conductivity in the chamber has stabilized and that the culture medium has been fully replaced by the EPN medium. This monitoring of impedance changes guarantees that all the experiments are made in a medium of appropriate conductivity. Figure 3a represents the size evolution of spheroids, measured on the ImageJ software, and calculated with the ratio of size after and before the experiment at several time points (2h, 1 day and 3 days after EPN), for groups submitted to EPN with or without bleomycin, and a control group without EPN. It shows that spheroid growth is inhibited in presence of the anti-cancer drug bleomycin, contrary to the control and EPN without bleomycin groups. This is also visible on figure 3b, along with the cell viability results 3 days after the experiment, suggesting mortality is higher in presence of bleomycin, as the red fluorescence is more present. the parameters of EPN used here, determined with previous experiment (data not shown), enable a reversible EPN as there are only few dead cells when spheroids are submitted to EPN only. Moreover, delivery of bleomycin in spheroids by EPN is efficient and has the expected impact on cell growth and viability.

OUTLOOK

After this proof of concept and first application, we intend to apply EPN of bleomycin to other types of cells, associated to cancerous cells in co-culture spheroids, and to optimize electrode and hydrogel designs in order to be able to monitor spheroid growth with bio-impedance measurements.

Figure 1 :

 1 Figure 1: (a) Formation of microwells of 200 µm diameter each in an agarose gel molded on a PDMS master. (b) Cell seeding in this scaffold illustrated with a X2.5 bright field image of the grown spheroids. (c) Integration into a microfluidic device composed of two facing electrodes with apertures for fluid inlet and outlet. The device is transparent, enabling in-situ confocal monitoring.

Figure 2 :

 2 Figure 2: (a) COMSOL model of the device enabling the electric field characterization. (b) Distribution of the electric field intensity in the device. (c) Study of medium diffusion in the micro-structured hydrogel by monitoring impedance when medium is injected.

Figure 3 :

 3 Figure 3 : (a) Evolution of the spheroids size after EPN with or without anti-cancer drug. (b) Representative images obtained by optical microscopy before EPN (first row), 3 days after EPN (2 nd row), and viability analysis with epifluorescence (3 rd row): Green= FDA staining (living cells); red=IP staining (dead cells).

ACKNOWLEDGEMENTS

This work was supported by the LABEX iMUST (ANR-10-LABX-0064) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). The authors gratefully acknowledge financial support from the Auvergne-Rhône-Alpes region under the Pack Ambition International funding (E-3DCell).


Zied Sassi

Nadia Fourrié

Vincent Guidard

Camille Birman

STUDY OF SATELLITE OBSERVATIONS SYNERGY IN ORDER TO IMPROVE SURFACE TEMPERATURE IN NWP

INTRODUCTION

This poster summarizes the main results of a preliminary study with as objective to assimilate land surface temperature (LST) in the surface analysis in Météo-France. More detailed results are available in [START_REF] Sassi | Use of Infrared Satellite Observations for the Surface Temperature Retrieval over Land in a NWP Context[END_REF]. The LST is a key parameter in Numerical weather prediction (NWP) particularly in surface-atmosphere energy balance modelization. Surface schemes use 2 m temperatures to estimate LSTs. The upper air analysis uses realistic LSTs retrieved from satellite observations to improve the assimilation of surface sensitive channels [Boukachaba, 2017][Guedj et al., 2011]. The objective of this research is to investigate the opportunity of using the retrieved LSTs from different sensors in the surface assimilation to improve the current surface analysis and the upper air assimilation. In this poster, we focus on IASI and SEVIRI infrared sensors.

METHODOLOGY

We used the LST retrieval files of AROME-France model (Applications de la Recherche à l'Opérationnel à Méso-Echelle) which is the operational limited area non hydrostatic model of Météo-France since December 2008. Figure 1 and Table 1 describe the domain, the assimilated data and the main characteristics of AROME-France model. The retrieval of LST from satellite observation in AROME-France model uses the mono-channel with known emissivity method [START_REF] Karbou | Microwave land emissivity and skin temperature for AMSU-A and AMSU-B assimilation over land[END_REF]. This method inverts the radiative transfer equation to calculate the surface temperature.

For IASI we used the channel 10.6µm and the emissivity atlas of the University of Wisconsin and for SEVIRI the channel 10.8µm and the emissivity atlas based on LAND-SAF data.

COMPARISON TO IN-SITU OBSERVATIONS

We compared SEVIRI LST to LST observations taken in two stations: Toulouse station situated in the suburb areas of Toulouse (France) and Evora station located 12 km south-west of Evora (Portugal) and which is one of the four KIT (Karlsruhe Institute of Technology) LST validation stations. Figures 2 and3 show good agreement between SEVIRI and in-situ LST diurnal cycles. SEVIRI LSTs represent in-situ LSTs better than the model especially for the extremes of the day. A higher standard deviations of SEVIRI LSTs (not shown) might be due to the different soil occupation properties between the used SEVIRI pixels. To better understand the differences between the two sensors LSTs, we plotted the RMSE of differences (Figure 5) for each season. Figure 5 shows higher values of RMSE during daytime. This might be due to higher impact of shadow/sunlit effect that can increase the differences between both sensors LSTs known their different angles of view. The comparisons of the four periods (Figure 6) show a better agreement during nighttime in all cases with high positive correlations (R > 0.96). Figure 6 shows also that a better agreement is found between the two sensors LSTs than between every sensor LSTs and the model LSTs except for daytime comparisons during Winter and Autumn periods where we find a better agreement of IASI LSTs with the model LSTs than with SEVIRI LSTs.

RADIANCE SIMULATION WITH RTTOV

To evaluate the impact of using one sensor's LST to simulate the other sensor's observed radiation, we have simulated the radiances of the 314 channels of IASI monitored at Météo-France using RTTOV model and compared them to the observed radiations.

CONCLUSIONS & PERSPECTIVES

This preliminary study showed a good agreement between SEVIRI LSTs and in-situ LSTs in Toulouse and Evora stations. The comparison of SEVIRI LSTs to IASI LSTs for clear colocalized data showed global agreement especially during nighttime. Finally, the comparison of IASI observed radiances to simulated ones using different values of LST showed better simulations with SEVIRI LSTs than with model LSTs especially during nighttime. These results are encouraging to use retrieved LSTs for further applications. The next step will consist in using LSTs to initialize the soil temperature in the current surface analysis system by updating the surface and deep soil temperatures using satellite retrieved LSTs in order to propagate the impact from an assimilation cycle to the next. Then, we will evaluate the impact of using the LSTs in surface analysis on forecasts and upper air assimilation.

FIGURE 1 -

 1 FIGURE 1 -AROME-France model domain and assimilated data

FIGURE 2 -

 2 FIGURE 2 -Diurnal cycle of SEVIRI LSTs, model LST and in-situ LST during summer period for Toulouse station

FIGURE 4 -

 4 FIGURE 4 -Diurnal variability of IASI and SEVIRI mean LSTs during Winter period. The blue line describes the number of available observations.

FIGURE 5 -

 5 FIGURE 5 -RMSE of difference between IASI and SEVIRI LSTs (in K) averaged on a 0.5 • × 0.5 • resolution grid.

FIGURE 6 -

 6 FIGURE 6 -Correlation coefficients and RMSE of difference of IASI and SEVIRI LSTs compared to each other and to model LSTs during daytime and nighttime (in K)

  Nighttime (a) and daytime (b) RMSE (K) related to simulations of IASI brightess temperatures using IASI LSTs (blue), SEVIRI LSTs (red) and model LSTs (green). Shaded areas correspond to channels which present very small sensitivity to LST.During nighttime (Figure7-a), SEVIRI LSTs gave better simulations and reduced the RMSE values with up to 1 K compared to model LSTs. During daytime (Figure7-b), the use of SEVIRI LSTs has less impact. This might be due to the higher differences between IASI LSTs and SEVIRI LSTs during daytime.

TABLE 1 -

 1 Main AROME-France model characteristics

ACKNOWLEDGMENT

I want to thank the Occitanie Region and Météo-France for their support to this research by funding my PhD. I thank also the KIT colleagues for sharing Evora station LST data and Météopole-Flux colleagues for Toulouse station data.

CONTACT INFORMATION

Zied Sassi

[Email] Zied.sassi@umr-cnrm.fr [Phone] +33 561 078 063