Laurent Bigaignon

Rémy Fieuzal

Claire Delon

Tiphaine Tallec

Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements

Keywords: Greenhouse Gases, Soil, Chambers, Crops, Agriculture

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Introduction

Nitrous oxide (N 2 O) is an important greenhouse gas as it presents a high global warming potential, around 300-fold higher than carbon dioxide (CO 2 ), for an approximate residence time of 120 years in the atmosphere (Intergovernmental Panel on Climate Change (IPCC), 2013). Moreover, N 2 O participates in stratospheric ozone depletion [START_REF] Ravishankara | Nitrous Oxide (N2O): The Dominant Ozone-Depleting Substance Emitted in the 21st Century[END_REF]. At the global scale, N 2 O is responsible for 6% of the current global warming, its atmospheric concentration has increased at a rate of 0.93 ppb per year from 2007 to 2017 (WMO Greenhouse gas bulletin, 2018) and represents 46% of the total greenhouse gas (GHG) emissions from agriculture (United Nations Environment Program (UNEP), 2012). Agricultural soils constitute the main anthropogenic source of N 2 O, mostly because of an increase in land conversion for agriculture and the intensified use of nitrogen fertilizers [START_REF] Ussiri | The Role of Nitrous Oxide on Climate Change[END_REF][START_REF] Lognoul | Impact of tillage on greenhouse gas emissions by an agricultural crop and dynamics of N2O fluxes: Insights from automated closed chamber measurements[END_REF][START_REF] Davidson | The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860[END_REF][START_REF] Snyder | Review of greenhouse gas emissions from crop production systems and fertilizer management effects[END_REF]. The formation of N 2 O in agricultural soils is mainly due to two microbial processes: nitrification and denitrification. Ammonium (NH 4 + ) and nitrate (NO 3 -) concentrations together with soil water content, temperature, organic matter and pH, are the principal physical-chemical factors modulating the production of N 2 O from agricultural soils [START_REF] Hénault | Predicting in situ soil N2O emission using NOE algorithm and soil database[END_REF][START_REF] Li | A process-oriented model of N2O and NO emissions from forest soils: 1. Model development[END_REF][START_REF] Parsons | Nitrate limitation of N2O production and denitrification from tropical pasture and rain forest soils[END_REF]Wiljer et Delwiche, 1954). N 2 O emissions are thus determined by a combination of factors. The magnitude of each of these direct controls is subject to its own set of biological and abiotic controls (agricultural practices),

showing the non-linearity of N 2 O emissions.

In the aim of mitigating climate change by reducing N 2 O emissions from soils, it is important to determine and understand N 2 O flux dynamics in response to variability in the climate and agricultural practices. To this end, different technologies have been developed and it is common to find studies with low sampling frequency for measuring N 2 O fluxes. The static chamber method has been widely used [START_REF] Reeves | Optimum sampling time and frequency for measuring N2O emissions from a rain-fed cereal cropping system[END_REF] as it presents the advantages of: (1) having low initial set-up costs, (2) being easily deployable from site to site and (3) being usable in small experimental plots, but it is also very demanding in manpower and offers a lower, more discontinuous sampling frequency than automated chambers (Zhang et al., 2014;[START_REF] Tellez-Rio | N2O and CH4 emissions from a fallow-wheat rotation with low N input in conservation and conventional tillage under a Mediterranean agroecosystem[END_REF][START_REF] Reeves | Quantifying nitrous oxide emissions from sugarcane cropping systems: Optimum sampling time and frequency[END_REF]Delon et al., 2017;[START_REF] Vinzent | Efficacy of agronomic strategies for mitigation of after-harvest N2O emissions of winter oilseed rape[END_REF][START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. The use of a static chamber requires the operator to be well synchronized with N 2 O flux events, which are not known a priori. Automated chambers have been developed to increase sampling frequency (e.g. four times a day) and to reduce the uncertainty related to temporal N 2 O flux variability [START_REF] Peyrard | N2O emissions of low input cropping systems as affected by legume and cover crops use[END_REF][START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. Nevertheless, due to field operations, instrument failure and/or quality control filtering, long periods of data loss may occur as for the static chamber method. Although the recent development of eddy-covariance methodology to measure N 2 O fluxes [START_REF] Nemitz | Standardisation of eddy-covariance flux measurements of methane and nitrous oxide[END_REF] presents the advantage of sampling at very high frequency (with a half-hourly time step) and at larger scale (from several m² to several hectares) than with chambers, it also presents the disadvantage of losing a lot of data because of low turbulence, especially during the night [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. So, whatever the methodology used, data loss is inevitable, which could be critical for studies of how the ecosystems function and for the calculation of annual N 2 O budget. While standardized methodologies for CO 2 flux gap-filling (mean diurnal variation, look up table, regression application) are well recognized and validated thanks to clear, robust relationships between the fluxes and drivers, those for N 2 O flux remain challenging and non-consensual [START_REF] Nemitz | Standardisation of eddy-covariance flux measurements of methane and nitrous oxide[END_REF]. The methodologies applied are often sitespecific and depend on the availability of ancillary data. A widely used gap-filling method in N 2 O emission studies is the linear interpolation technique [START_REF] Tellez-Rio | N2O and CH4 emissions from a fallow-wheat rotation with low N input in conservation and conventional tillage under a Mediterranean agroecosystem[END_REF][START_REF] Vinzent | Efficacy of agronomic strategies for mitigation of after-harvest N2O emissions of winter oilseed rape[END_REF]. However, N 2 O fluxes have high temporal variability [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF] [START_REF] Papale | A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization[END_REF][START_REF] Melesse | Artificial neural network application for multi-ecosystem carbon flux simulation[END_REF][START_REF] Moffat | Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes[END_REF][START_REF] Dengel | Testing the applicability of neural networks as a gap-filling method using CH4 flux data from high latitude wetlands[END_REF]. [START_REF] Moffat | Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes[END_REF], who reviewed 15 different techniques tested on a dataset compiled from six different European forest sites to fill gaps in values of the half-hourly Net Ecosystem Exchange (NEE), showed that ANN had a consistently good gap-filling performance and low annual sum bias, with R² between 0.41 to 0.90, and recommended its use even in different ecosystems. [START_REF] Melesse | Artificial neural network application for multi-ecosystem carbon flux simulation[END_REF] tested ANN to gap-fill hourly CO 2 fluxes from three different ecosystems (forest, wheat, grassland) in the United States. They found that this technique could successfully predict observed values with R² values between 0.75 to 0.94 and therefore considered it reliable, efficient and highly significant to estimate CO 2 fluxes.

However, the very fleeting N 2 O fluxes are more difficult to simulate and gap-fill as, unlike CO 2 fluxes, they do not present any diurnal dynamics [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. ANN is a statistical method that provides a relatively easy way to approach non-linear functions. It has the advantage of being usable without any knowledge of the processes that have an impact on the targeted variable(s).

Some knowledge on the key drivers for thematic applications, as for N 2 O fluxes gap-filling, is still needed. Today, few studies have used and evaluated the performance of ANN methodology with other methodologies simulating N 2 O fluxes [START_REF] Ryan | The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem[END_REF][START_REF] Villa-Vialaneix | A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops[END_REF][START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF]. [START_REF] Ryan | The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem[END_REF] undertook to simulate daily N 2 O emissions from an intensive grassland ecosystem in New Zealand using ANN methodology and obtained good statistical results, with R² ranging from 0.67 to 0.85. [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] compared the use of ANN and linear interpolation methodologies to gap-fill a 6 years time series of N 2 O flux measured on a Canadian crop site following a corn/soybean/winter wheat rotation. On average, whatever the cropping year, they found a lower R² with the use of ANN than [START_REF] Ryan | The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem[END_REF]. Depending on the cropping year and the crop species, R² ranged from 0.19 (for a soybean crop) to 0.65 (for a maize crop). This strong variation in the ANN performance may question the relevance of using that methodology and underlines the need to more investigate it in different context and configuration. However, [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] recommended the use of ANN instead of the classical interpolation method, as on average the former scored a higher R² (0.41) than the latter (0.34) when considering the entire studied period.

The study proposed here is part of this dynamics, as comparisons between gap-filling procedures on N 2 O fluxes are still lacking. Its objectives are twofold: (1) to evaluate and compare linear interpolation and ANN as gap-filling methodologies to reproduce daily low and high N 2 O fluxes and (2) to propose a third gap-filling approach combining both methodologies using a criterion on the method's choice, either linear interpolation or ANN, based on the duration of the missing data period and the N 2 O flux magnitude. All three methodologies were tested on a long time series of measurements taken on an agricultural plot (maize/wheat rotation) in the southwest of France from 2012 to 2016 and compared together according to each crop plot functioning period (bare soil, growing season, low or high N 2 O fluxes intensity, etc.). The effect of the methodology's choice on the estimated annual N 2 O budget is discussed.

Materials

Experimental site

The study is based on a dataset collected at the Lamasquère site (FR-Lam) This experimental plot is part of the Regional Spatial Observatory, the regional ZA PYGAR (Zone Atelier Pyrénées-Garonne), the national research infrastructure OZCAR (Critical Zone

Observatories: Research and Applications; [START_REF] Gaillardet | OZCAR: The French Network of Critical Zone Observatories[END_REF] and the European network ICOS (Integrated Carbon Observation System). The site is therefore fully equipped with instruments to monitor greenhouse gas fluxes, meteorological, radiation and soil variables [START_REF] Béziat | Carbon balance of a three crop succession over two cropland sites in South West France[END_REF][START_REF] Tallec | Crops' water use efficiencies in temperate climate: Comparison of stand, ecosystem and agronomical approaches[END_REF]. Vegetation dynamics are also monitored in detail with green area, biomass and height measurements.

Management of the crop plot is intensive, with exportation of all aboveground biomass for mulching the cowsheds and/or feeding the herd. An annual irrigation of approximately 150 mm is applied when maize is cultivated. The total amount of nitrogen (N) applied varies from 100 to 200 kgN ha -1 and 110 to 145 kgN ha -1 for mineral and organic fertilization respectively. Input modalities vary according to the crop species. Mineral fertilization is split into 3 applications for wheat and applied once for maize. The FR-Lam site occupation followed the rotation maize (2012), wheat (2013), cover crop (CC) (2013), maize (2014), maize (2015), wheat (2016), CC (2016).

N 2 O flux measurement

To measure N 2 O emissions, 6 stainless steel automated chambers (covering an area of 0.161 m²) were installed on the plot according to a closed dynamic set-up. The chambers were elongated (70cm×23cm×10cm) so as to be easily inserted in the crop inter-rows, where they were placed at a 10 cm soil depth. A previous comparison study between automated chambers and eddy-covariance methodologies [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]) on the same site showed that a set of 6 automated chambers was sufficient to integrate spatial heterogeneity of the site and to well capture the mean daily N 2 O flux dynamics. The set-up measured N 2 O accumulation in each chamber alternately during 17.5 minutes every 6 hours, i.e. four cycles a day (00:00, 06:00, 12:00, 18:00). A pump maintained continuous air circulation in and out of the chamber at a constant flow rate (1 L.min -1 ). Sampled air was supplied from the chamber to a gas analyser that measure N 2 O molar fraction every 10 seconds (Thermofisher 46i, Megatec, France). The calculated N 2 O fluxes (exponential fitting) were filtered by means of a mixture of goodness-of-fit statistics and visual inspection [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. A fan also enabled air homogeneity in the chamber.

Daily N 2 O fluxes were calculated as the mean value of all available fluxes during the day.

The choice to work at a daily timescale was made as no significant diurnal N 2 O flux intensity variation was found on FR-Lam site in a previous study [START_REF] Tallec | N2O flux measurements over an irrigated maize crop: A comparison of three methods[END_REF]. Because of hardware dysfunction and/or technical operations, such as tillage and harvest, chambers were removed many times, leading to missing data which means that the number of available N 2 O flux measurement per day to calculate a daily N 2 O flux varied from 0 to 24. In this study, in order to maximize the size of the dataset for the need of that study we kept all available daily fluxes, even if calculated with only one measurement a day. Finally, from 2012 to 2016, out of 1827 daily N 2 O flux values, 1442 were acquired. The whole dataset included 63% of "highly" representative daily values (calculated with 12 to 24 measurements a day), 22% of "moderately" representative daily values (calculated with 6 to 12 measurements a day) and 13% of potentially "poorly" representative daily values (calculated with 1 to 6 measurements a day).

Meteorology, soil variables and ancillary data measurements

The rich dataset acquired on FR-Lam offers the possibility to test different combinations of possible explanatory variables for the input layer in the ANN and to choose the most explanatory (see section 3.1.2.3). Matter and energy fluxes, meteorological data, radiation and soil variables were available at a half-hourly time step and are described in Table 1. The measurement methodology for each variable is described in Beziat et al. ( 2009) and [START_REF] Tallec | Crops' water use efficiencies in temperate climate: Comparison of stand, ecosystem and agronomical approaches[END_REF]. Each variable was aggregated at a daily time scale. Vegetation data were collected from 5 to 6 times a year during the growing season and a simple linear interpolation was applied to obtain the daily dynamics.

Table 1. Description of variables potentially available for gap-filling

Unfortunately, soil mineral nitrogen content was not available for all years and with low temporal resolution. Thus, to include the fertilization effect on N 2 O emissions in the ANN construction, a decreasing exponential function N t (Eq. 1) was developed to account for the effect of fertilizer on N 2 O fluxes. The choice of coefficient 0.10 was determined based on the visualisation of the function's temporal dynamic. The aim of this function is to memorize a fertilisation event over multiple days after a fertilisation as we noticed, based on observations, that N 2 O fluxes appears several days after a fertilisation event if the environmental conditions are met. No optimisation process was undertaken to find the most accurate coefficient value.

(1)

Here t represents the number of days after fertilization, N t is the nitrogen function value at day t after fertilization (kgN ha -1 ) and N 0 is the quantity of nitrogen fertilizer applied by the farmer on day 0 (kgN ha -1 ).

Methods

ANN gap-filling methodology

General principle

In this study, the ANNs created are based on the algorithm proposed by [START_REF] Bishop | Neural Networks for Pattern Recognition[END_REF] (i.e., multi-layer perceptrons implemented in the nnet R package) and are composed of three layers of neurons (input, hidden and output) connected by synapses (Fig. 1). The input layer represents the predictive variables selected by the user to explain the targeted variables of the output layer. The number of input parameters and hidden neurons is determinant in the creation of a neural network and to avoid over fitting. Not enough hidden neurons will make it difficult to reach an accurate estimation of the targeted variable, while too many will lead to noisy estimates [START_REF] Delon | Soil NO emissions modelling using artificial neural network[END_REF]. The relationship between input and output variables is modelled by sigmoid functions connecting neurons, the weights of interactions being determined through an iterative procedure.

During the model training, nnet uses supervised learning algorithms. The synapse weights are modified to fit the network in order to reduce an error function (i.e., Sum of Squared Errors (SSE)) corresponding to the difference between predicted and observed output [START_REF] Guenther | neuralnet: Training of neural networks[END_REF][START_REF] Ryan | The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem[END_REF] following the Broyden-Fletcher-Goldfarb-Shanno algorithm. The values of the weights are initialized with random values and progressively adjusted.

Creation of the neural network step by step

Neural Network performances

The data preparation procedure is established in accordance with the following studies Lek et al. 1966 andOlden et al. 2002, which recommend: (1) to standardize the measurement scales of the network inputs (by subtracting the mean value and dividing by the standard deviation)

especially when the measurements are of different orders of magnitude, and (2) to convert the After normalization, the data set was randomly partitioned into two independent sets of equal data amount, 50% of the data to train and 50% of the data to test the created ANN, that enables to test the ANNs generalisation efficiency on an important amount of data. This 50/50 partitioning operation was repeated 40 times with the objective of obtaining 40 different random draws and selecting the draw with the highest performance.

The performance levels of the ANNs were evaluated using the determination coefficient (R², Eq. 2.), the Root Mean Squared Error (RMSE, Eq. 3.) and the Relative Root Mean Squared Error (RRMSE, Eq. 4.).

R² =

(2)

RMSE = (3) RRMSE = (4)
With the number of observed values, the observed value, the predicted value and and the mean of observed and predicted data, respectively.

Different ANN configurations were tested. An overall neural network was implemented first on the whole dataset and then by discriminating each crop growing season and bare soil periods.

Early stopping procedure

In order to avoid over or under fitting during ANN training, an early stopping procedure was setup to determine the best number of iterations required to build the ANN model. For every 40 draws, ANNs were created and trained throughout 1 000 iterations and saved every 10 iterations (in total 4000 created ANNs for one functioning period). Then the 4000 trained ANNs were tested on the testing datasets. The R² and RMSE between observations and simulations on the training and testing dataset were also saved every 10 iterations for the 40 draws. The number of required iterations to build the best ANN model and select the best draw was determined in function of the minimum value of RMSE obtained on the testing datasets. Fig. 2 illustrates the case of draw number 5 for maize 2012 period.

For that draw, the ANN created after 770 iterations was selected as it corresponds to the minimum RMSE of the testing dataset. Below or above that value the ANNs may be under or overfitted. The best ANN determined from the iteration procedure was then kept and saved for every 40 draws of each functioning period. Then a classification of the 40 random draws was done in function of R² in order to select the draw showing the best R². A final test to select the best ANN for a functioning period was then made by comparing the ANN simulation on the test dataset with the corresponding observations dynamic. If this visual dynamic comparison was not satisfying, the second ANN and draw set with the best R² result was chosen, and so on.

Selection of input variables

The methodology used to select the most appropriate combination of variables for each period is presented in Fig. 3. Only input variables (Table 1) without any missing values were considered.

A linear regression calculation between each variable and N 2 O fluxes was then performed to select a combination of variables showing the highest correlation with N 2 O fluxes (highest R² values). Afterwards, numerous ANNs were created by testing different combinations of the selected variables for each functioning period. The final input combination was selected based on the previously described process using the criterion R 2 , RMSE and visual checking. Finally, a specific ANN was built for each functioning period using a combination of 4 or 5 input variables, and 3 hidden neurons to fit the output variable, i.e. the daily soil N 2 O fluxes. Increasing the number of input parameters and/or hidden neurons did not improve the results significantly (data not shown).

Comparison between ANN and linear interpolation gap-filling methodologies

In order to evaluate the linear interpolation methodology and compare it with the ANN methodology on exactly the same days of observations, artificial gaps were created into the dataset used for the testing procedure (Fig. 4) for each functioning period. Each artificial gap was then filled using the linear interpolation method between the surrounding training values. The linear interpolation methodology was evaluated statistically using the same indices as described in section 3.1.2.1 (R², RMSE, RRMSE).

Determination of the GMD (Gap Magnitude and Duration) coefficient

The comparison between ANN and linear interpolation methodologies showed that each technique may have benefits and limitations depending on the gap condition (see section 4.5).

Therefore, the best gap-filling approach would be to combine the two techniques with the help of a new criterion, the coefficient GMD (Eq. 5) which would allow the most suitable methodology to be chosen. The value of GMD depends on the length of the gap and on the difference between the N 2 O fluxes intensity bracketing the gap. GMD thus allows the best gap-filling methodology to be chosen between linear interpolation and ANN.

GMD (5)

Where is the number of consecutive missing days in the gap and and are the N 2 O flux values observed at the beginning and the end of the gap, respectively. A decision tree to guide the choice to use either the linear interpolation or the ANN gapfilling methodology to fill a specific gap, depending on the GMD value, is developed in Fig. 5.

Estimating annual N 2 O budget uncertainty due to gap-filling

The uncertainty of the annual N 2 O budget related to the gap-filling was investigated using a methodology adapted from [START_REF] Richardson | A method to estimate the additional uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record[END_REF]. This methodology is usually used to estimate annual NEE gap-filling uncertainty [START_REF] Richardson | A method to estimate the additional uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record[END_REF][START_REF] Schmidt | The carbon budget of a winter wheat field: An eddy covariance analysis of seasonal and inter-annual variability[END_REF]. It is calculated by adding random gaps as actually observed in each real site-year of data gaps and then by adding small and long gaps (the maximum gap length is 8 days). However, in this methodology, the data set specificity is not taken into account, i.e. the number, length and locations of gaps are not respected. To include this information, a new methodology was

Results and Discussion

N 2 O flux range and dynamic at FR-Lam crop site

Over the five years of monitoring, the magnitude and timing of measured N 2 O fluxes varied according to the soil occupation, the agricultural practices and the climatic year (Fig. 8). Typical variability of N 2 O fluxes occurred according to rain, irrigation and fertilisation events. Wheat, cover crops and bare soil periods emitted less N 2 O, on average (from 6.6 to 13.0 gN ha -1 d -1 ), than the maize periods (16.3 to 42.9 gN ha -1 d -1 ) (Table 2). Intensity of N 2 O fluxes was also higher in the maize periods than in the others, with maximum values ranging between 160 and 399, compared to 33 and 133 gN ha -1 d -1 (Table 2, Fig. 8). Maximum fluxes measured on maize 2012 (174 gN ha -1 d -1 ), maize 2015 (160 gN ha -1 d -1 ) and wheat 2013 (33 gN ha -1 d -1 ) were in the range of those given for wheatirrigated maize crop rotations in [START_REF] Dhadli | N2O emissions in a long-term soil fertility experiment under maize-wheat cropping system in Northern India[END_REF] for northern India on a loamy sand soil site (143 and 50 gN ha -1 d -1 for maize and wheat respectively) and in [START_REF] Han | Effects of Reduced Nitrogen Fertilization and Biochar Application on CO 2 and N 2 O Emissions from a Summer Maize-Winter wheat Rotation Field in North China[END_REF] for northern China (96 and 50 gN ha -1 d -1 for maize and wheat respectively).

Maximum fluxes measured on wheat 2016 (100 gN ha -1 d -1 ) and maize 2014 (399 gN ha -1 d -1 )

were significantly above those generally reported in the literature. kgN ha -1 applied at an interval of only one week, and high soil water content, with two irrigation events one week after N application. We can assume that N 2 O fluxes on our site were within the average range of other wheat-maize crop rotation sites but atypical management practices or climate may have led to temporally unusual and important N 2 O fluxes.

The bare soil and wheat 2013 periods were the ones with the most and the least missing data, 21% and 1% respectively. The number of consecutive missing daily N 2 O fluxes varied from 1 to 62, with a minimum of 1 to 2 on wheat 2013 and a maximum of 1 to 62 on bare soil periods (Table 2). These gaps were due to the removal of chambers for field operations or disturbance of the aero-dynamic conditions, leading to a filtering of the data.

ANN simulation and selected variables

The selected input variables varied among the study periods (Table 3). Whatever the combination, all input variables selected for gap-filling (Table 3) had a direct or indirect influence on N 2 O emissions [START_REF] Robertson | Nitrification and denitrification in humid tropical ecosystems: potential controls on nitrogen retention[END_REF]. Fig. 8 illustrates connections between input variables and the direct proximal factors of denitrification regulation (inspired from [START_REF] Robertson | Nitrification and denitrification in humid tropical ecosystems: potential controls on nitrogen retention[END_REF].

Variables related to vegetation structure make the greatest contribution to modulating proximal factors and thus to N 2 O emission modulation. LAI and h_veg are naturally related to the soil mineral nitrogen availability. These also modulate and reflect the soil dioxygen content via root respiration, the soil water content via potential evapotranspiration (ETP) and the soil carbon content via rhizodeposition and physical disruption by the roots, etc. Net radiation (Rn) corresponds to the available radiative energy in the top of soiltop of canopy continuum, which naturally depends on Rg but also on the crop coverage and structure, and it potentially modulates the heat fluxes that are implicated in soil water loss and soil heating. Sensible heat flux (H) may be linked to soil temperature as an increasing H is associated with surface heating coming from water stress. Finally, the fertilization parameter uses the function established in Eq. 1. to represent nitrogen application in the field. [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] partly used the same parameters in all their single-year ANNs, together with air temperature, soil temperature, soil water content, NO 3 -concentration, NH 4 + concentration with snow depth, rainfall, the season and the time of the year. In contrast with [START_REF] Ryan | The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem[END_REF] and [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] who both used rainfall as controlling factor, waterfall (rain and irrigation), even tested in applying a lagtime from 0 to 5 days, did not appear to be a significant input parameter in the ANN constructions at FR-Lam.

SWC at 50 cm depth appeared more explanatory than rainfall. Moreover, since the water table rose frequently near the soil surface at FR-Lam, SWC at 50 cm parameter reflects environmental conditions' variation related to sub-ground phenomenon on that site in addition to being integrative of the rainfall/irrigation amount. The season and the time of the year were not necessary parameters, as we separated our dataset according to the functioning period, and snow depth was not useful as no significant snowfall occurred during the monitoring. NO 3 -and NH 4 + concentrations would have been valuable as determining parameters in the formation of N 2 O (Fig. 8) but they were not continuously monitored.

An ANN using LAI, Ta_mean, Rn, ETP and SWC_50 as input variables was first tested for the whole period from 2012 to 2016 and led to poor statistical scores with an R² of 0.28, an RMSE of 23 gN ha -1 d -1 and an RRMSE of 189%. We thus separated the data set according to the functioning periods (Table 3) to create a neural network specific to each crop growing season period and a single one combining all bare soil periods with the most explanatory variables. The combination of variables giving the best estimation of N 2 O emissions varied from one period to another (Table 3).

ANN performances

Creating ANNs relating to each functioning period proved to be relevant, with R², RMSE and RRMSE ranging from 0.54 to 0.94, from 4.0 to 33.9 gN ha -1 d -1 and from 62 to 115, respectively (Table 4), far better than the single ANN over the whole period. The R² scores are highly improved compared to those of [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] who found on average R² values ranging from 0.19 to 0.65, depending on the cropping year. Two main reasons can explain that important improvement. First, in our study, an ANN model was built specifically for each functioning period and by discriminating bare soil from growing season periods, whereas [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] built ANN model without discriminating functioning periods. Even if discriminating functioning period makes more difficult the genericity of the ANN algorithm, it is in favour of improving the fineness of the gap-filling. Then, [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] used scores and results averaged over 44 scenarios for each cropping year whereas in our study, only the best ANN model was kept from the 40 random draws for each studied period and shown here.

Considerable disparity occurred on statistical results according to the functioning period (Table 4). The RRMSE appeared to be high when looking at all functioning periods together and highlighted the fact that the RMSE was of the same magnitude as the average N 2 O fluxes in average. With R² ranging from 0.85 to 0.94, periods with maize or cover crop showed better performance than periods with wheat or bare soil, showing an R² ranging from 0.54 to 0.66. Even

though wheat 2016 and bare soils had an RMSE of the same order of magnitude as the other periods, the difference between the observations and the test simulations turned out to be significant during these two periods when related to the average N 2 O fluxes with the highest RRMSE values, 115 and 114 % respectively. Wheat and bare soil periods also corresponded to the lowest mean daily N 2 O fluxes: 6.6 -7.5 gN ha -1 d -1 against 9.3 -42.0 gN ha -1 d -1 during maize/cover crop periods (Table 2). These results show that the ANN method would simulate fluxes with high intensity variation, as observed in maize crops, better than fluxes with low intensity variation as observed in winter wheat. However, these scores are close to those reported in the literature for CO 2 flux gap-filling on a forest site [START_REF] Melesse | Artificial neural network application for multi-ecosystem carbon flux simulation[END_REF][START_REF] Moffat | Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes[END_REF]. Accordingly, ANN performances for N 2 O flux estimation proved to be relevant even without any diurnal cycle when compared to other CO 2 flux modelling studies.

Evaluation of ANN and linear interpolation methodologies gap-filling performances

Considering all functioning periods together, ANN methodology gave better statistical scores (see Table 4) than the linear interpolation with higher R² (0.84 vs 0.68) and lower RMSE (12.4 vs 17.4 gN ha -1 d -1 ) and RRMSE (101 vs 141 %). In the same trend, [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF] obtained better statistical scores for the ANN methodology than with the linear interpolation methodology, finding an average R² of 0.41 and 0.34 respectively.

Table 4. R² and RMSE calculated on testing dataset for ANN, linear interpolation and both methodologies combined for each functioning period.

Even if the linear interpolation might give comparable scores, i.e. for maize in 2012 and 2015, it proved to perform better when the averaged N 2 O fluxes were the lowest, i. e. wheat 2013, wheat 2016 and bare soil periods.

Effect of gap-filling methodologies on mean N 2 O emissions according to functioning period

To analyse the effects of the two different gap-filling methodologies on the mean N 2 O fluxes per period, we applied them separately to gap-fill the real gaps from the studied data set from

2012 to 2016. In the case of the ANN methodology, there are no significant differences on the means and standard deviations of the N 2 O fluxes before and after gap-filling whatever the period considered, which shows no large N 2 O flux variability was created after gap-filling (Table 5). In contrast, large variability appears after gap-filling when the linear interpolation method is used during maize 2014 and bare soil periods : the mean N 2 O flux for the maize 2014 period before gap-filling is 42.9 ± 77.7 gN ha -1 d -1 and becomes 64.5 ± 107.2 gN ha -1 d -1 after gap-filling; for the bare soil periods, the mean and standard deviations are 7.5 ± 15.0 gN ha -1 d -1 before gapfilling and 21.3 ± 58.0 gN ha -1 d -1 after gap-filling. In both cases, the linear interpolation methodology tends to increase mean N 2 O fluxes. Based on these results, it appears that either the ANN underestimates, or the linear interpolation overestimates N 2 O fluxes, or both are wrong.

However, it questions the use of one methodology or the other. To explain these strong differences between the two methodologies, we analysed these specific periods in greater detail.

In some specific case, applying a linear interpolation gap-filling on the bare soil periods would introduce an unrealistic continuity such like the period from 06. shown) were not in favour of N 2 O production: only 75 mm of rain in 67 days, where the ANN simulated very low N 2 O fluxes.

Combination of ANN and linear interpolation methodologies

As the linear interpolation method also gives satisfactory statistical results (Table 4), it would have been a loss to set it aside since we have seen that, in some circumstances, this method is better than ANN, especially on periods with low averaged N 2 O fluxes. Moreover, in some specific case, like long period of missing data or period with environmental conditions in favour of N 2 O emissions, applying a linear interpolation gap-filling could introduce unrealistic N 2 O fluxes. In order to fix this, we propose to combine the two gap-filling methodologies depending on the value of GMD ((Eq. 5.), see section 3.3). The RMSE are then compared with GMD values, and used to highlight the potential threshold GMD values on which the selection of the best methodology to gap-fill the data could be based (Table 6). Two GMD threshold values were highlighted (Table 6): when GMD < 14, the RMSE of the ANN method was higher than the one from linear interpolation; when 14 < GMD < 30, RMSE were quite similar for both; when GMD > 30, the RMSE of the ANN method was lower than linear interpolation one.

These results suggest that the suitability of a methodology for gap-filling depends on the GMD value. The linear interpolation is preferable when GMD < 14 and the ANN method is preferable when GMD > 30. When 14 < GMD < 30, the use of either interpolation or ANN is possible as both methodologies give approximately the same score. We decided to use the ANN methodology in this case as it allowed capturing the effect of environmental variables on N 2 O fluxes. However, in the case of long gaps (when a gap contains more than 15 missing data) bracketed by two low N 2 O fluxes with GMD < 14, the decision would be to use the linear interpolation methodology. It would be wise to look at possible important environmental modifications during the gaps, such as heavy rain, irrigation or fertilization events, where it could be more accurate to use the ANN methodology instead.

The combined methodology gave practically the same statistical results over the whole period as those from the ANN methodology with a R² and a RMSE of 0.84 and 12.4 gN ha -1 d -1 for both (Table 4). However, an improvement is seen on the three periods where the linear interpolation

Comparison of the three gap-filling methodologies: linear interpolation, ANN and the combination of both

The simulated N 2 O flux dynamic were compared between the three methodologies applied for maize 2014 period (Fig. 10). It showed that the ANN methodology gave on average better statistical scores than the linear interpolation (Table 4) and better reproduced the important peak than did the linear interpolation around the 29 June 2014. However, ANN over estimated low 4), it allowed a better catching of N 2 O flux dynamic (Fig. 10 c).

In the same way, during periods where the linear interpolation methodology gave better results than the ANN one (Table 4), combining both methodologies helped to better reproduce low fluxes (Fig. 11) and to improve statistical results compared to the ANN with a R², a RMSE and a RRMSE of 0.69, 7.8 gN ha -1 d -1 and 102 for wheat 2016, respectively.

The ANN and the combined methodologies gave on average comparable statistical scores whatever the functioning period probably due to a control by high N 2 O flux values. However, because the combined methodology proved to be more efficient to capture N 2 O intensity dynamic it was used to gap-fill missing data from 2012 to 2016 (Fig. 7).

Evaluation of gap-filling uncertainty on N 2 O budget

The uncertainties on the N 2 O annual budget related to the gap-filling varied between ± 25 and ± 198 gN ha -1 among years, and between 0.9 and 4.1 % when looking at the relative uncertainty (Table 7) calculated via the methodology described in section 3.4. Gap-filling had a particularly low impact on year 2013 (Table 7) as there were only few gaps during this period: 3 gaps with respectively 1, 6 and 1 missing value(s). These results are relatively low compared to the annual uncertainties calculated by [START_REF] Taki | Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil[END_REF], who obtained an uncertainty variation among years of ± 70 to ± 810 gN ha -1 and 7 to 24 % when looking at the relative uncertainty using ANN as gap-filling method, and an uncertainty variation among years of ± 40 to ± 470 gN ha -1 and 5 to 15

% when looking at the relative uncertainty using linear interpolation as the gap-filling method.

These differences with our results could be explained by the different methods adopted. Their results also prove that a combination of both methodologies may improve the gap-filling as their N 2 O annual budget uncertainties were lower with the linear interpolation than with the ANN methodology, despite the fact that they found a better R² for the ANN. These uncertainty differences among years can be explained by the disparities in the number and size of gaps and in the intensity of N 2 O emissions among years: years with a high N 2 O budget (related to high N 2 O fluxes), especially observed during maize periods, and with a small number of gaps lead to a low gap-filling uncertainty whereas the uncertainty increases when the length of gaps increases and N 2 O flux intensity is low, especially for wheat and bare soil periods. Nevertheless, the results

show that the proposed mixed gap-filling methodology performs well when used on annual N 2 O flux datasets with a relative uncertainty below 5 % for each year.

Conclusion

Three different gap-filling methodologies were examined with respect to their performance for filling gaps in long-term N 2 O flux data series. This study has demonstrated that the use of an artificial neural network could be a helpful tool to gap-fill daily N 2 O fluxes as already recommended for other greenhouse gases such as CO 2 and CH 4 . On average, ANN proved to give better gap-filling performance than linear interpolation when looking at the R² and RMSE scores, even though linear interpolation performed better when gap-filling N 2 O fluxes of low intensity.

Thus, the study proposes a new approach to gap-fill N 2 O emissions by combining the advantages of both methodologies. Such a procedure, which is not very costly in computing time and in requested variables (4 to 5 in our case), may be very helpful for future works on N 2 O emission analysis, especially on sites where numerous continuous variables are monitored (fluxnet and ICOS sites for example). In particular, it may improve the calculated N 2 O budget with limited gap-filling uncertainty, below 5% in this study. Moreover, given the strong non-linearity of N 2 O emissions due to multiple interactions between controlling environmental variables, ANN has the advantage of reproducing a complex process without any a priori knowledge of the system, and is flexible and easy to use. The GMD thresholds developed in this study figures to be site specific as they highly depend on the ANNs performances and thus on quality and number of explanatory variables that may strongly vary among study sites. That saying, as the eddy covariance methodology is becoming more and more used across agricultural sites and for processes studies, besides the fact that higher available dataset could improve the methodology performance, further evaluation of the mixed gap-filling methodology should be carried out for N 2 O fluxes measured and compiled at higher resolution than the day (e.g. 6-hourly data).

Fig. 1 .

 1 Fig. 1. Schematic MLP with 4 input variables, 3 hidden neurons and 1 output variable

Fig. 2 .

 2 Fig. 2. RMSE (gN ha -1 d -1 ) between ANN training (black) and testing (grey) datasets in function of iterations for draw number 5 during maize 2012 period. Here the minimum RMSE for the test simulation is reached after 770 iterations.

Fig. 3 .

 3 Fig. 3. Diagram outlining the successive steps to implement the ANN methodology.

Fig. 4 .

 4 Fig. 4. Method used to create and linearly interpolate artificial gaps from the N 2 O observations.

Fig. 5 .

 5 Fig. 5. Decision tree helping to select the more suitable gap-filling method according to GMD value.

Fig. 6 .

 6 Fig. 6. Diagram outlining the successive steps to calculate the uncertainty due to gap-filing methodology.

Fig. 7 .

 7 Fig. 7. Daily N 2 O fluxes dynamic observed (cross) and gap-filled (dot) at FR-Lam from 2012 to 2016. M: maize; B: bare soil; W: wheat; C: cover crop; N: nitrogen application (kgN ha -1 ); I: irrigation.

Fig. 8 .

 8 Fig. 8. Illustration of the possible interactions between selected variables and proximal factors of N 2 O emissions. Variables in grey italics were not measured or not available.

Fig. 9 .

 9 Fig. 9. N 2 O fluxes (+) gap-filled by ANN (o) and linear interpolation (Δ) on bare soil period from 05/03/2014 to 19/05/2014 and on maize 2014 period from 20/05/2014 to 09/06/2014.

N 2 O

 2 fluxes measured in early July (Fig. 10 a. and b) where the linear interpolation reproduced much better their low intensity (Fig. 10 b). Although the combined methodology gave approximately the same statistical scores than those of ANN alone (Table

Fig. 10 .

 10 Fig. 10. Comparison between observed (cross) and simulated (dot) N 2 O fluxes dynamic according to gap-filling methodology ANN (a), linear interpolation (b) and mixed methodology (c) on Maize 2014. Only the days used for the test dataset are shown here.

Fig. 11 .

 11 Fig. 11. Comparison between observed (cross) and simulated (dot) N 2 O fluxes dynamic according to gap-filling methodology ANN (a), linear interpolation (b) and mixed methodology (c) on Wheat 2016. Only the days used for the test dataset are shown here.

  and sporadic events can lead to dynamics totally different from the smoother diurnal variation observed for CO 2 . N 2 O flux dynamics are too complex to use the same gap-filling methodology as that applied for CO 2 fluxes. Low sampling frequency of N 2 O fluxes combined with a linear interpolation gap-

	filling method may thus result in the real N 2 O flux dynamics being missed, under-or
	overestimation of the annual N 2 O budget and misinterpretation of the effects of climate and
	management.
	To overcome this issue, and in the context of the ICOS (Integrated Carbon Observation

System;

[START_REF] Franz | Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe´s terrestrial ecosystems: a review[END_REF]) network, Nemitz et al. (2018)

suggested using and testing the Artificial Neural Networks (ANN) methodology that had proved to perform well for CH 4 and CO 2 flux gap-filling

Table 2 .

 2 Description of the daily N 2 O flux dataset at FR-Lam per functioning period.

Table 3

 3 Summary of the selected variables used to develop the different neuronal network at FR-Lam.

Table 5 .

 5 Effect of ANN and linear interpolation gap-filling methodologies on mean N 2 O emissions (gN ha -1 d -1 ).

Table 6 .

 6 RMSE according to gap-filling methodology and GMD coefficient.method has better statistical results with the combined methodology compared to the ANN one with a R², a RMSE and a RRMSE of 0.68, 8.6 and 113 and of 0.66, 8.6 and 114 respectively for bare soil periods, of 0.69, 7.8 and 102 and of 0.60, 8.8 and 115 respectively for wheat 2016 and

	of 0.67, 3.5 and 57 and of 0.54, 4.0 and 65 respectively for wheat 2013. These results support the
	use of a combined methodology instead of applying either the linear interpolation alone or the
	ANN methodology alone for gap-filling.

Table 7 .

 7 N 2 O budget gap-filling uncertainty per civil year. Mean and standard deviation were calculated using the 100 repetitions.

Acknowledgement

Research for this article was supported by a public grant received from the French Ministry of Higher Education, Research and Innovation. Data acquisition at FR-Lam are mainly funded by the Institut National des Sciences de l'Univers (INSU) through the ICOS and OSR SW observatories. Facilities and staff are funded and supported by the Observatory Midi-Pyrenean, the University Paul Sabatier of Toulouse, CNRS (Centre National de la Recherche Scientifique), CNES (Centre National d'Etude Spatial) and IRD (Institut de Recherche pour le

Développement). We are grateful to Franck Granouillac, Nicole Claverie and Bartosz Zawilski for their technical support, advice, and valuable assistance in the field and site management, respectively and Aurore Brut for the data processing. Special thanks to the Ecole Supérieure d'Agriculture de Purpan for accommodating our measurement devices in their field.


Nico Dissmeyer

Olivier Coux
email: olivier.coux@crbm.cnrs.fr

Manuel S Rodriguez

Rosa Barrio
email: rbarrio@cicbiogune.es

PROTEOSTASIS: A European network to break barriers and integrate science on protein homeostasis

Protein homeostasis (proteostasis) is at the core of cellular functions. The European network PROTEOSTASIS was created to steer research and foster collaborations in the interconnected fields of posttranslational modifications by Ubiquitin-family members and protein turnover by proteasome, autophagy and lysosomal systems in health and diseases, across the kingdoms of life.

Proteostasis -The Topic

Proteostasis -the portmanteau of protein and homeostasis -is the term for the underlying molecular network balancing the entire proteome of a living organism. PROTEOSTASIS is also the running name of our recently terminated COST Action BM1307 "European network to integrate research on intracellular proteolysis pathways in health and disease" from the Biomedicine and Molecular Biosciences area of COST (European Cooperation in Science and Technology, see Text Box, funding period April 2014-April 2018; cost-proteostasis.eu).

The average number of proteins per individual cell is substantial and differs among species. In the single yeast cell, there are roughly 6,000 different proteins [START_REF] Ho | Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome[END_REF] summing up to 42 to 50 million protein molecules [START_REF] Ho | Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome[END_REF][START_REF] Milo | What is the total number of protein molecules per cell volume? A call to rethink some published values[END_REF][START_REF] Milo | Cell biology by the numbers[END_REF], while in human cells there are more than 10,000 different proteins per cell for a total of 2 to 3 billion protein molecules at any given time [START_REF] Nagaraj | Deep proteome and transcriptome mapping of a human cancer cell line[END_REF][START_REF] Kulak | Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells[END_REF]. Possible proteoforms occur on top of those numbers with variable concentrations and diverse distributions throughout the cell types of multicellular organisms. This striking complexity requires tight regulation on multiple levels, which is achieved though robust and highly sophisticated mechanisms. Indeed, broadly more than 5% of all proteins throughout the kingdoms are involved in protein synthesis and turnover, highlighting the global importance of these processes [START_REF] Hartl | Molecular chaperones in protein folding and proteostasis[END_REF][START_REF] Kim | Molecular chaperone functions in protein folding and proteostasis[END_REF][START_REF] Vierstra | The ubiquitin-26S proteasome system at the nexus of plant biology[END_REF]. Despite much progress over the past decades, understanding how these complex proteomes are constantly and dynamically monitored and remodeled remains an enormous challenge.

Proteostasis refers to the biological mechanisms controlling the biosynthesis, coand posttranslational processing, folding, trafficking, neofunctionalization, i.e. changes in protein function, and degradation of proteins in vivo. Among these processes, intracellular proteolysis is not only critical for survival and cellular homeostasis of all living cells under unchallenged conditions, but also for rapid proteome remodeling in response to many environmental stresses. Furthermore, disturbed proteostasis may lead either to the accumulation of normally degraded proteins or to excessive protein degradation. Those can be associated with ageing and many pathologies, such as cancer, immune and neurological disorders in humans, as well as other diseases and responses to environmental stresses in plants and other organisms across the kingdoms of life.

Overall, since proteostasis plays pivotal roles in almost every biological process, including general growth and development in all organisms and mechanisms as diverse as heart development and plant tolerance to flooding, for example, its analysis has elevated the interest of scientists from diverse backgrounds and with different research foci. A wide variety of research approaches is used to identify the mechanisms that regulate proteostasis, typically involving a breadth of model organisms (fungi, invertebrates, vertebrates or plants) and different methodologies. This heterogeneity might restrain researchers into their own scientific niche, impairing knowledge exchange between fields that are in fact linked in real life. The PROTEOSTASIS network was built upon the conviction that connecting the different areas of research in this field would result in a boost of scientific exchange and sharing, and set the stage for an advance of our community knowledge through new possibilities of exchange and collaboration.

One central aspect of proteostasis is protein degradation. Often referred to as proteolysis, it has been studied for decades and publications on this topic are constantly increasing since the 1970s. Two major intracellular proteolysis pathways, namely the Autophagy-Lysosomal Pathway (ALP) and the Ubiquitin-Proteasome System (UPS), synergistically play critical roles inside cells for the maintenance of cellular homeostasis and organism (patho)physiology. Thanks to the ever increasing understanding of these pathways, and also to recent technical developments in cell imaging, proteomics and more generally "-omics" studies, we now realize that these research fields are strongly entwined and coordinated.

[Figure 1 near here]

The small 8-kDa 'ubiquitous' protein Ubiquitin (Ub), is a common endogenous molecular tag working as posttranslational protein modification used by both UPS and ALP to control the stability of most if not all proteins in a highly specific and regulated manner. Due to its central role in cell metabolism, Ub is thus commonly accepted as a critical signature for the engagement of these sophisticated intracellular machineries. However, Ub also exerts proteolysisindependent functions, such as regulation of signaling pathways, transcription or protein trafficking. Also in the ERAD (ER-associated protein degradation) system, Ub plays an important role in cargo retrotranslocation that finally also concludes in proteolysis via the proteasome. The field expanded by understanding that Ub is just a member of a family of UbLs (Ub-Like proteins) that cooperate together to fine-tune the intracellular proteome. The most studied UbLs, such as SUMO (small Ub-like modifier) or NEDD8 (neural precursor cell expressed, developmentally down-regulated 8), are extremely versatile and crosstalk, i.e. functionally interact in a dynamic manner with various modes of posttranslational modifications of target proteins by Ub, modulating the fate of cellular proteins in response to external and intracellular stimuli.

PROTEOSTASIS -The Network

Even if we still do not clearly understand when, how and why many proteostatic systems function and collaborate within the cell, they enable alternative solutions to complicated problems, ranging from protein degradation to relocalization and neofunctionalization, and increase cellular plasticity for adaptation and survival in a continuously changing environment. Addressing all of these aspects of proteostasis was the ambition of the PROTEOSTASIS network.

To be able to dissect the molecular bases of complex proteostatic systems, in the health and disease of multiple species and with numerous approaches, we nurtured synergies between research groups to bridge the gap between parallel but closely related fields, bringing together researchers working on various processes in different organisms, using diverse but complementary approaches. By reaching hundreds of European scientists working on protein homeostasis, PROTEOSTASIS offered the possibility to develop a large exchange space that broke artificial but real barriers, thus unleashing connectivity and creativity in our field.

The COST Action PROTEOSTASIS coordinated and integrated efforts made by research teams in different areas to translate novel discoveries into products of clinical and/or economical value. In other words, PROTEOSTASIS grew a fertile ground to impulse a comprehensive and holistic approach to tackle scientific challenges, by capitalizing on our diversity and complementary expertise, potentiating the resources and competencies available in each group.

As a networking tool, PROTEOSTASIS was successful in engaging more than 270 research groups, including researchers from 30 different countries covering all scientific areas of proteostasis. The main goal of PROTEOSTASIS was to foster exchanges between fields that tended to ignore each other for years by facilitating collaborative research and broad scientific exchange. To bring together expertise from different disciplines and to openly discuss recent scientific developments, researchers from diverse backgrounds were invited to actively participate and join the community. Special emphasis was placed on the transfer from basic to translational science, novel technologies, and training next-generation scientists. Sharing unpublished data was the rule during the meetings organized by PROTEOSTASIS in the last four years. This strongly promoted collaboration among experienced and early stage academic, clinical and industry-based researchers.

PROTEOSTASIS was structured around six wide thematic blocks, covering a broad and complementary scientific spectrum within protein homeostasis. Protein Modification dealt with molecular and structural properties of proteostatic components; Proteolytic Systems gathered scientists around mechanisms and structures of intracellular proteolytic systems like proteasomes, autophagy, lysosomes, and apoptosis; Cell Signaling focused on signaling cascades, protein trafficking, and transcriptional regulation; Quality Control covered folding and misfolding, chaperones, aggregation, and ERAD; Cell Proliferation and Differentiation touched many aspects of cell cycle, cell growth, and development; and Diseases and Biotechnology explored the molecular basis of diseases and biomarkers in cancer, inflammation and neurodegeneration, addressing drug targets and biotechnology.

Activities, Outcomes and Highlights

Whether from the academic or the private sector, the PROTEOSTASIS network helped scientists to develop fundamental or translational research thanks to the multiple instruments that it offered to the community, namely the organization or co-organization of 14 scientific meetings and workshops, eight training schools, and 47 scientific staff exchanges (STSMs). Additional dissemination instruments included the website (cost-proteostasis.eu), webinars, newsletters, and social media (Figure 2). The co-organization of workshops and other activities together with relevant European or national bodies in the life sciences such as EMBO, FEBS, and other scientific societies was instrumental. The meetings, workshops and conferences covered broadly the topics of the proteasome and signalosome complexes, autophagy, aging and neurodegeneration, apoptosis and cell death, cell polarity and movement, vesicular biology, system biology, Ub, SUMO and other UbLs, N-end rule pathway, protein degradation in plants, agronomy, biotechnology, and bioeconomy. Two examples of these meetings, ZOMES IX -"PCI complexes and ubiquitin defining a hub for protein homeostasis" and "N-term 2017 -Proteostasis via the Nterminus", were described in meeting reports elsewhere [START_REF] Alpi | ZOMES: the intriguing interplay of PCI complexes and the ubiquitin in protein homeostasis[END_REF][START_REF] Dissmeyer | N-term 2017: Proteostasis via the N-terminus[END_REF]. The eight training schools aimed to teach the analysis of in vitro and in vivo processes of ubiquitylation and SUMOylation, the proteostatic basis of ageing and redox regulation of metabolic processes and Ub-assisted autophagy.

Regarding networking, PROTEOSTASIS facilitated the consolidation or the initiation of collaborations and the generation of novel international consortia dedicated to specific issues. Those resulted in more than 50 joint publications in peer-reviewed scientific journals plus three thematic books "Proteostasis", "SUMO" and "Plant Proteostasis" with additional 74 contributions of network members [START_REF] Matthiesen | Proteostasis : methods and protocols[END_REF][START_REF] Rodriguez | SUMO : methods and protocols[END_REF][START_REF] Lois | Plant proteostasis : methods and protocols[END_REF]. Importantly, the international scientific exchanges enabled the mobility of researchers between laboratories and the development of joint research projects that were successfully funded. Examples of those are TrainERS (train-ers.eu) on endoplasmic reticulum stress; META-CAN (metacan.eu), combining cancer metabolism, cell death, cancer immunity, data analysis and immunometabolism; UbiCODE (ubicode.eu), deciphering the mechanistics of the Ub code; or TRIM-NET, exploring the functions of TRIM E3 ligases (Figure 2). Future joint applications are in the planning stages among members of the network.

[Figure 2 near here]

Conclusions

The goal of PROTEOSTASIS was to foster and concentrate interdisciplinary scientific exchange and development of novel research ideas. The network coordinated and integrated the efforts made by research teams to better understand protein homeostasis and facilitated the interchange with the private sector. The network has contributed significantly towards reversing the national and scientific fragmentation of research efforts within the European research area and beyond. After four exciting years of activities, we look back at a wonderful time of multifaceted exciting research, exchange of data and ideas, networking, training and numerous newly built interactions and joint collaborations. PROTEOSTASIS as a network, including its social media and website activities, is continued after the ending of the funding period. It will serve as a platform to communicate science, assist to organize meetings and conferences, and launch proposals in relevant funding calls. We aim to preserve those in the long term to facilitate and foster future interactions. We believe this type of network will be useful to other areas of research that might experience field fragmentation.

Cristea and Andrea Tortajada, the Project Manager Ms Emilia Moreira and Dr Rosa Barrio's research group for all the support in order to achieve a successful project.

RESOURCES

i cost-proteostasis.eu ii www.cost.eu/actions/BM1307 iii twitter.com/Proteostasis iv www.youtube.com/channel/UCmQZiJ9a4OfS97T6Q2-g_sA v www.facebook.com/Proteostasis-839198092811311/

Supplemental Information

Supplemental Information associated with this article can be found in the online version.

Figure Captions

Figure 1 .

 1 Figure 1. A scientific field in constant development: PROTEOSTASIS areas of study.

Figure 2 .

 2 Figure 2. Achievements and milestones of the PROTEOSTASIS network -an overview of activities and new funded networks.

ACKNOWLEDGEMENTS

This article is based on the work of COST Action BM1307-European network to integrate research on intracellular proteolysis pathways in health and disease (PROTEOSTASIS; http://www.cost.eu/COST_Actions/bmbs/BM1307), funded by COST (European Cooperation in Science and Technology, www.cost.eu). The publication of this article was financed by the COST Final Action Dissemination (FDA) Grant. We thank and acknowledge the participation and contribution of all BM1307 PROTEOSTASIS participants, activities organizers, and the Grant Holder institution CIC bioGUNE (Asociación Centro de Investigación Cooperativa en Biociencias). Special thanks are addressed to the COST Office, especially the Scientific Officer Dr Inga Dadeshidze and the Administrative Officers Gabriela