Angéla La
email: la.angela.sc@gmail.com

Patrick Perré

Behnam Taidi

Process for symbiotic culture of Saccharomyces cerevisiae and Chlorella vulgaris for in situ CO2 mitigation

Keywords: Photo-bioreactor, Flow cytometer, Medium design, Autotrophy, Heterotrophy, Co-dominant culture

Industrial biotechnology relies heavily on fermentation processes that release considerable amounts of CO2.

Apart from the fact that this CO2 represents a considerable part of the organic substrate, it has a negative impact on the environment. Microalgae cultures have been suggested as potential means of capturing the CO2 with further applications in high value compounds production or directly for feed applications. We developed a sustainable process based on a mixed co-dominant culture of Saccharomyces cerevisiae and Chlorella vulgaris where the CO2 production and utilization controlled the microbial ecology of the culture. By mixing yeast and microalga in the same culture the CO2 is produced in dissolved form and is available to the microalga avoiding degassing and dissolution phenomena. With this process, the CO2 production and utilization rates were balanced and a mutual symbiosis between the yeast and the microalga was set-up in the culture. In this study the reutilization of CO2 and growth of C. vulgaris was demonstrated. The two organism populations were balanced at approximately 20×10 6 cells ml -1 and almost all the CO2 produces by yeast was reutilized by microalga within 168 h of culture. The C. vulgaris inoculum preparation played a key role in establishing co-dominance of the two organisms. Other key factors in establishing symbiosis were the inoculum ratio of the two organisms and the growth medium design. A new method allowed the independent enumeration of each organism in mixed culture. This study could provide a basis for the development of green processes of low environmental impact.

Introduction

Industrial biotechnology such as bioethanol production, alcoholic beverage production and liquid effluent treatments form a considerable part of human biological activity. These industries, involving fermentation technology, release large quantities of CO2. At the same time, the current trend is towards more sustainable industrial processes. Waste recycling is increasingly seen not only as an obligation but an opportunity in industry.

Commercially, the loss of a considerable part of the substrate in the form of CO2 is an inefficient practice that cannot be avoided with microbial cultures. CO2 mitigation from exhausts gases could provide an opportunity where the substrate would be entirely used at the same time rendering the process sustainable. To this end, photosynthesis is the best candidate to be associated to the normal production process. This is present in nature, often based on symbiotic relationships between organisms, and in some industrial sectors such as sewage treatment.

Biotransformation can be achieved through the use of GMO organisms. Alternatively, biotransformation can be performed by the use of specific consortia to create the desired microbial ecology. The ability to control mixed cultures is key to the use of consortia for biotransformation.

The term "symbiosis" is credited to Heinrich Anton de Bary who first used and described it as "the living together of unlike named organisms" in 1879 [START_REF] Oulhen | English translation of Heinrich Anton de Bary's 1878 speech, 'Die Erscheinung der Symbiose' ('De la symbiose')[END_REF]. One of the most studied natural composite organisms, considered as the model of symbiosis, is the lichen. Lichen arises from a symbiotic relationship between a fungi and algae or cyanobacteria [START_REF] Gargas | Multiple origins of lichen symbioses in fungi suggested by SSU rDNA phylogeny[END_REF] and the metabolites obtained from lichens have application in industries such as pharmaceutical industry [START_REF] Müller | Pharmaceutically relevant metabolites from lichens[END_REF].

Co-culture systems based on symbiosis between microbial species have been attempted for biotechnological applications in bioprocess and environmental protection [START_REF] Santos | Microalgal symbiosis in biotechnology[END_REF][START_REF] Magdouli | Co-culture for lipid production: Advances and challenges[END_REF]. The choice of microbial species (microalgae, bacteria or yeast) depends on the final aims of co-culture: harvesting by bioflocculation [START_REF] Subashchandrabose | Consortia of cyanobacteria/microalgae and bacteria: Biotechnological potential[END_REF][START_REF] Rai | Biohydrogen production from cheese whey wastewater in a two-step anaerobic process[END_REF], wastewater treatment [START_REF] Arumugam | Biohydrogen and polyhydroxyalkanoate co-production by Enterobacter aerogenes and Rhodobacter sphaeroides from Calophyllum inophyllum oil cake[END_REF], production of extracellular polymeric substances [START_REF] Haggstrom | Regulation of a mixed culture of Streptococcus lactis and Saccharomycopsis fibuliger[END_REF] or growth promotion and lipid production [START_REF] Milledge | A review of the harvesting of micro-algae for biofuel production[END_REF][START_REF] Pragya | A review on harvesting, oil extraction and biofuels production technologies from microalgae[END_REF].

Reports of studies on symbiotic co-cultures of microalgae and yeast have been increasingly appearing in the scientific literature, with the aim of improving biomass and target molecule productivity. These co-cultures fall into two categories: studies with bioreactors in series where the exhaust gases from the heterotrophic culture are fed into the autotrophic culture, and studies where both yeast and microalgae are concomitantly in the same culture. We have decided to refer to the former as coupled cultures and the latter as mixed cultures (Fig. 1).

Coupled cultures consists of an upstream heterotrophic yeast-culture connected to an autotrophic culture of microalgae in photo-bioreactor through the exhaust gases from yeast culture [START_REF] Puangbut | Integrated Cultivation Technique for Microbial Lipid Production by Photosynthetic Microalgae and Locally Oleaginous Yeast[END_REF][START_REF] Santos | Enhanced lipidic algae biomass production using gas transfer from a fermentative Rhodosporidium toruloides culture to an autotrophic Chlorella protothecoides culture[END_REF][START_REF] Dillschneider | Combination of algae and yeast fermentation for an integrated process to produce single cell oils[END_REF][START_REF] Chagas | Production of carotenoids and lipids by Dunaliella tertiolecta using CO2 from beer fermentation[END_REF]. Studies on coupled cultures have suggested an increase in the final microalgae biomass and lipid production that is achieved by effectively enriching the air supply to the microalgae cultures with CO2 from the heterotrophic culture. In a coupled-culture system, the autotrophic organism benefits from the heterotrophic organism with no positive or negative impact on the latter, therefore the symbiosis is commensal. The mixed culture system of microalgae and yeast focuses on the symbiotic potential of associating both organisms in the same culture. This system has an advantage over coupled-cultures in that it provides an opportunity for direct gaseous exchange in dissolved form bypassing the dissolution and degassing rates of the gas supply. Usually, any gas supplied to a bioreactor has to pass from a gaseous phase into a liquid phase (dissolution) and the gases produced by the culture have to pass from the liquid phase into the gaseous phase (degassing). These transfers are subject to specific surface limitations as well as mixing phenomena that can limit CO2 supply for the autotroph and O2 supply for the heterotroph in a coupled culture. In a mixed culture of microalgae and yeast, each organism would use the gas produced by the other organism in situ and without passing through a gaseous phase, the organisms would benefit from each other, so the symbiosis based on these gas exchanges would be mutual.

From a CO2 mitigation viewpoint, as the heterotrophic CO2 production rate is usually largely superior to it autotrophic consumption, the two populations must be balanced in such way so that the photosynthetic population can cope with the rate of CO2 production. Hence the heterotrophic activity must be in step with the CO2 removal rate. This could be achieved though co-dominance of the populations allowing synergy between the two organisms based on gaseous exchange. So far, no scientific studies have been published with the stated aim of developing co-dominant symbiotic mixed cultures.

One of the main challenges for a mixed culture of yeast and microalgae appears to be the dominance of one organism over the other by the end of incubation period. The dominance seems to be due to the use of a culture medium that preferentially promotes either the growth of the yeast or that of microalgae [START_REF] Dong | In situ carbon dioxide fixation in the process of natural astaxanthin production by a mixed culture of Haematococcus pluvialis and Phaffia rhodozyma[END_REF][START_REF] Cai | Comparisons of growth and biochemical composition between mixed culture of alga and yeast and monocultures[END_REF][START_REF] Xue | A new strategy for lipid production by mix cultivation of Spirulina platensis and Rhodotorula glutinis[END_REF][START_REF] Cheirsilp | Mixed culture of oleaginous yeast Rhodotorula glutinis and microalga Chlorella vulgaris for lipid production from industrial wastes and its use as biodiesel feedstock[END_REF][START_REF] Shu | Enhancing high quality oil accumulation and carbon dioxide fixation by a mixed culture of Chlorella sp. and Saccharomyces cerevisiae[END_REF][START_REF] Zhang | Synergistic effects of oleaginous yeast Rhodotorula glutinis and microalga Chlorella vulgaris for enhancement of biomass and lipid yields[END_REF]. In [START_REF] Zhang | Synergistic effects of oleaginous yeast Rhodotorula glutinis and microalga Chlorella vulgaris for enhancement of biomass and lipid yields[END_REF], a mixed culture of yeast Rhodotorula glutinis and microalga Chlorella vulgaris showed a yeast dominance of 88 % of the total population after 2.5 days of growth (18 g l -1 of yeast and 2.4 g l -1 of microalgae). Additionally, the maximum biomass concentration reached by the yeast in monoculture was 8.5 times higher than of the microalga in monoculture (14.5 and 1.7 g l -1 respectively), this suggests that the medium designed for mixed culture was more suitable for yeast than microalgae. Inversely in [START_REF] Cai | Comparisons of growth and biochemical composition between mixed culture of alga and yeast and monocultures[END_REF], the microalga Isochrysis galbana was dominant in the mixed culture at the end of the experiment (97 % of the total population). In this study, the medium seems to have been more adapted to the microalgae rather than yeast Ambrosiozyma cicatricose, leading to microalgae dominance. In these studies, on mixed cultures, no mention of the enumeration method in the mixed culture is made.

The present study was conducted to develop a co-dominant and symbiotic mixed culture of two model organisms: the yeast Saccharomyces cerevisiae and the microalga Chlorella vulgaris. In order to promote codominance between the two organisms, a new growth medium was specifically designed, and the inoculum ratio was adjusted. The two species were grown in the same medium and in a non-aerated photo-bioreactor fitted with a fermentation lock to prevent gas exchange with the outside atmosphere. The absence of external air supply is intended to force the mutual symbiosis through synergetic effects of in situ gas exchange. To monitor the proportion of populations, a flow cytometric method was used to determine the cell concentration of each population in the mixed-culture. Dissolved O2 and CO2 were continuously measured in-line to evaluate the in situ gas exchange between the two species and to proof the mutual symbiosis.

Through this study, we propose a general methodology for the design of a co-dominant symbiotic mixed culture of a heterotroph and an autotroph in general and assess the success and the challenges of such strategy.

The work presented here was performed on well-known model organisms but can provide the basis for more applied studies. The potential advantage of this work is that a symbiotic mixed culture would self-regulate the speed of the bioconversion hence the CO2-production and -utilization rates; it could potentially eliminate the need for gas supply and can lead to full utilization of the substrate. The potential savings would be those of recovering the cost of the portion of the substrate that is normally lost as CO2, making considerable savings in terms of gas supply avoidance and reducing environmental CO2 emissions. In an economical assessment, all these savings would have to be weighed against the losses incurred by moderating the bioconversion speed in step with the photosynthetic rate.

Materials and methods

Strategy of this study

A diagram (Fig. 2) demonstrates the strategy used to establish co-dominant culture of S. cerevisiae and C. vulgaris.

Microbial strains and their maintenance

S. cerevisiae strain ID YLR249W was supplied by Life Technologies-University of California San Francisco. This clone expresses a cytoplasm fusion protein coupled to a green fluorescent protein (GFP). The protein of interest is the translation elongation factor 3 encoded by the gene YEF3 [START_REF] Qin | Isolation of the Yeast Gene Encoding Elongation Factor 3 for Protein Synthesis[END_REF]. The strain was maintained on YPG agar stock plates incubated at 25°C for 3 days and subsequently stored at 4°C for 3 months before subculture. The YPG agar medium was composed of (g l -1 ): yeast extract (10), peptone (20) glucose (10) and agar (15) and the stock plates were renewed every three months.

C. vulgaris SAG 211-12 was obtained from the Culture Collection of Algae (SAG), University of Göttingen, Germany. The strain was maintained in liquid culture (50 ml in 250 ml flask) through weekly subculture into fresh medium, incubated at 25°C on an orbital shaker (120 rpm) with continuous lighting at 20 µmol m -2 s -1 at the surface of the culture and in air enriched with 1.5% (v/v) CO2. The liquid inorganic medium used was

Design of a specific medium for mixed culture

Monocultures of S. cerevisiae and C. vulgaris were grown in three different media in order to define a medium suitable for co-dominance of the organisms in mixed culture. The media were based on different combinations of the microalgae growth medium (MBM) (described above) and components from the commonly used yeast growth YPG (yeast extract, peptone and glucose) medium (g l -1 ):

-MBM-G: MBM medium with glucose (10) -MBM-GY: MBM medium with glucose (10) and yeast extract (10) -MBM-GP: MBM medium with glucose (10) and peptone (20)

Erlenmeyer flasks (50 ml working volume; 250 ml total volume) were used for the monoculture of C. vulgaris and S. cerevisiae in the above media and the inoculation ratio was 1% (v/v) from a fully-grown culture. The flasks were incubated at 25°C on an orbital shaker (120 rpm) with continuous lighting at 80 µmol m -2 s -1 (LI250A Light Meter; LI-COR, USA) at the surface of the cultures. Yeast monocultures were conducted for 3 days and microalgae monocultures for 5 days.

The medium finally selected and specifically designed for the mixed culture was named MBM-GP and was composed of (mg l -1 ): NaNO3 (1,500); CaCl2.2H2O (50); MgSO4.7H2O (150); FeEDTA (40); K2HPO4 (75); KH2PO4 (175); NaCl (20); H3BO3 (2.86); MnCl2.4H2O (1.81); ZnSO4.7H2O (0.220); CuSO4.7H2O (0.08);

MoO3 85% (0.036); CoSO4.7H2O, (0.09), glucose (10,000); peptone (20,000).

Cultures in photo-bioreactors

All experiments in photo-bioreactor (PBR) were conducted in a stirred bioreactor (5-liter working volume) (BIOSTAT Bplus -5 L CC; Sartorius Stedim biotech, Göttingen, Germany). The PBR was lit with six LED lamps (Ledare 130 lumen, 2700 Kelvin, 27° dispersion angle, IKEA, Leiden, Netherlands). The light intensity at the inner surface of the bioreactor for each lamp was measured at 1,600 µmol m -2 s -1 (LI250A Light Meter; LI-COR, USA). The stirring speed was 750 rpm with a 3-blades pitch-blade impeller (UniVessel 5l, Germany), each inclined at 45°from the horizontal axis. The planar diameter of the impellers was 65 mm. The temperature was maintained at 25°C and the pH was controlled at 6.5 with automatic base KOH

(1 mol l -1 ) or acid H3PO4 (1 mol l -1 ) solutions addition based on the continuous measurements made by an internal pH probe (EasyFerm PLUS K8 325, Hamilton, Bonaduz, Switzerland). Dissolved oxygen (pO2) in cultures was measured with an internal probe (VisiFerm DO H2, Hamilton, Bonaduz, Switzerland). The pO2 was expressed in terms of % of O2 partial pressure in the liquid phase of the culture.

The S. cerevisiae and C. vulgaris specific growth rates (µ) were calculated as the slope of the linear part of the logarithm of cell concentration plotted versus time.

Mixed cultures in PBR

Two non-aerated mixed cultures in PBR were grown using MBM-GP medium. The experimental set up (Fig.

3

) involved hermetically isolating the bioreactor to limit the exchange of gases with the atmosphere at the exterior of the bioreactor.

Dissolved CO2 (pCO2) was measured only in the mixed culture No. 1 with an external minisensor integrated in a flow cell (CO2 Flow-Through Cell FTC-CD1, PreSens, Regensburg, Germany). The culture was circulated (90 ml min -1 ) through the flow cell with the aid of a peristaltic pump (520S/R, Watson Marlow) and back into the bioreactor. The flow-through cell was placed as close to the outlet from the bioreactor as possible. The passage of the culture over the sensor in the flow cell allowed the continuous measurement of pCO2 via an optical fiber. As with the pO2, the pCO2 was expressed in % of CO2 partial pressure in the liquid phase of the culture.

S. cerevisiae inoculum preparation was the same for both mixed cultures; S. cerevisiae was grown on MBM-GP medium, at 25°C, for 2 days. The preparation of the C. vulgaris inocula for the two mixed cultures differed; for the mixed culture No. 1, the C. vulgaris inoculum was grown on autotrophic MBM medium under continuous illumination, for 15 days, at 25°C and for mixed culture No. 2 the C. vulgaris inoculum was grown on heterotrophic MBM-GP medium under continuous lighting, for 15 days, at 25°C.

Monoculture of S. cerevisiae in PBR

The monoculture of S. cerevisiae was grown in a non-aerated PBR in MBM-GP medium, with culture parameters as described above and the photo-bioreactor configuration was the same as for mixed culture (Fig.

3

), there was no aeration and gas outlet was closed as described with a fermentation lock. The culture was lit as for mixed culture. The S. cerevisiae inoculum was grown in MBM-GP medium, at 25°C, for 2 days.

Monocultures of C. vulgaris in PBR

Two monocultures of C. vulgaris in PBR were grown, one in heterotrophic MBM-GP medium and the other in autotrophic MBM medium. For the first one the inoculum was prepared in MBM-GP medium, the second one using MBM medium and both under continuous light at 25°C for 15 days. Both culture conditions were set up as described above and the photo-bioreactor was continuously aerated with sterile air (Midisart 2000 0.2 µm PTFE, Sartorius, Göttingen, Germany) at 500 ml min -1 (0.1 vvm) (1 atm, 25°C).

The impact of ethanol on C. vulgaris growth

C. vulgaris was grown on MBM medium in Erlenmeyer flasks (50 ml working volume; 250 ml total volume) and the flasks were incubated at 25°C on an orbital shaker (120 rpm) with continuous lighting at 20 µmol m -2 s -1 and in air enriched with CO2 1.5% (v/v). Four ethanol concentrations were tested (0, 2, 4 and 6 g l -1 ) (ethanol 96 %).

Analytical methods

Simultaneous enumeration of C. vulgaris and S. cerevisiae by flow cytometry

A flow cytometer (Guava easyCyte TM , EMD Millipore, Burlington, United States) was used to simultaneously determine the cell concentrations of S. cerevisiae and C. vulgaris in mixed culture. The excitation wavelength of the blue laser was 488 nm and detectors separately captured the Forward-scattered light (FSC) that is proportional to cell-surface area (size), the Side-scattered light (SSC) that indicated particles granularity and the fluorescence emitted by the cell (auto-fluorescence). Samples were diluted so that the cell enumeration was always performed at cell concentrations between 1×10 5 and 1×10 6 cells ml -1 . The method for cell enumeration by flow cytometer suspensions containing only one of the microorganisms was previously validated against a Thoma counting chamber as the referent method (data not shown). Cell viability of C.

vulgaris was also determined by flow cytometry using the Guava ViaCount Reagent (EMD Millipore, Burlington, United States).

Glucose and ethanol measurements

Culture supernatants were prepared by sample centrifugation (10 min, 3500 g), filtration (PTFE Syringe Filter 0.2 µm, Fisherbrand, Waltham, United Stated) and High Pressure Liquid Chromatography (HPLC) (Ultimate 3000, Thermo Scientific, United States). A cationic column (Aminex HPX-87H, Bio-Rad, United States) was used with 2 mM sulfuric acid as the mobile phase with a flow rate of 0.5 ml min -1 , an injection volume of 10 µl, a temperature of 45°C and a pressure of 60 bar. Detection was by means of a refractive index (RI) detector (RI 101, Shodex, Japan).

Dry weight

The dry weight was determined by sampling and centrifuging 10 ml of culture (10 min and 1,800 g). The pellet was washed with an equal volume of deionized water, and was centrifuged again (10 min, 1800 g) and the final pellet was transferred into a dry pre-weight ceramic cup (24 h, 105 °C). The pellet was dried overnight at 105 °C and cooled in a desiccator containing dry silica gel prior to weighing. A correlation between the dry weight and the cell concentration was established for S. cerevisiae and C. vulgaris: DWyeast = 3.25×10 -8 Nyeast (9 data points and R²=0.91) and DWalgae = 1.5×10 -8 Nalgae (13 data points and R²=0.96) with DW the dry weight (g l -1 ) and N the cell concentration (cells ml -1 ). The experimental data points for the yeast were obtained from a monoculture in PBR using the MBM-GP medium and for the microalgae from a monoculture in PBR using the autotrophic medium MBM.

CO2 production and consumption

CO2 produced by yeast was assumed to be the main cause of culture acidification, resulting in the automatic addition of base (KOH) under the experimental conditions designed to keep the pH at 6.5. Consequently, the quantity of KOH solution is directly proportional to the CO2 produced and was used to estimate the amount of CO2 produced by yeast. The difference in the KOH added into the yeast monoculture and the mixed culture indicated the amount of CO2 used by C. vulgaris and was used to perform a carbon mass balance.

Results

Validation of simultaneous enumeration of C. vulgaris and S. cerevisiae by flow cytometry

A method for separately-enumerating C. vulgaris and S. cerevisiae populations in a mixed suspension was developed. The two species were distinguished on the basis of their specific auto-fluorescence detected by flow cytometry. C. vulgaris cells were distinguished through chlorophyll fluorescence (emission wavelength of 650 nm) and S. cerevisiae with the fluorescence of the constitutively expressed GFP protein (emission wavelength of 525 nm) (Fig. S1).

To validate the method, eleven mixed suspensions were prepared over a range of precise microalgae:yeast ratios (reference ratios) and the two populations in the mixed suspensions were measured with flow cytometry (experimental ratios). By plotting the experimental C. vulgaris ratio as a function of the referent microalgae ratio (Fig. 4), a linear relationship was obtained with a slope of 1.048 (correlation coefficient of 0.997; 11 data points). A linear relationship was also found for S. cerevisiae with a slope of 0.996 and a correlation coefficient of 0.998, validating the method for enumerating microalgae and yeast simultaneously in mixed suspensions.

Strategy for a co-dominance of C. vulgaris and S. cerevisiae in mixed culture

Design of a specific medium for mixed culture

A growth medium that allowed the growth of both organisms was necessary. According to Fig. 5, MBM-G medium allowed only microalgae growth and S. cerevisiae growth was barely detectable. Monocultures of C.

vulgaris and S. cerevisiae in MBM-GY medium showed the opposite results from those in MBM-G medium:

MBM-GY allowed good growth of S. cerevisiae but not of C. vulgaris.

In MBM-GP medium, both C. vulgaris and S. cerevisiae could grow: the maximum C. vulgaris population was 2×10 8 cells ml -1 and the maximum yeast population was 10 times lower (2×10 7 cells ml -1 ).

Definition of parameters for mixed culture in PBR

The temperature and pH in PBR were chosen to favor C. vulgaris growth. According to [START_REF] Kumar | Enhanced CO2 fixation and biofuel production via microalgae: Recent developments and future directions[END_REF], temperatures of 15-26°C and neutral pH is optimal for microalgae growth. The form of the dissolved CO2 concentration and the pH of the culture are directly linked so we chose to control the pH at 6.5 to achieve a good compromise between having a neutral pH and the dissolved CO2 and bicarbonate species proportioned at around 0.5 at 25°C [START_REF] Edwards | Vapor-liquid equilibria in multicomponent aqueous solutions of volatile weak electrolytes[END_REF]).

The inoculum ratio was set up in way to minimize dominance of yeast and favor microalgae growth:

X0 C.

Monocultures in PBR

Yeast and microalgae monocultures in PBR served as reference cultures for mixed culture. In the case of C.

vulgaris, two reference conditions were tested: heterotrophic growth in the presence of glucose and, autotrophic growth in the absence of glucose. The behavior of the individual species in mixed cultures would then be compared to the latter reference conditions.

Monoculture of S. cerevisiae in closed and non-aerated PBR

S. cerevisiae was grown on MBM-GP medium in PBR without aeration, exactly under the same conditions as for the subsequent mixed culture.

The yeast exponential growth phase (µ= 0.27 h -1 ) occurred within the first 24 h of incubation (15 h of exponential phase) and was accompanied with glucose and O2 consumption. S. cerevisiae used all glucose within the first 31 h of incubation reaching a maximum population of 2.2×10 7 cells ml -1 (Fig. 6). Within the first 31 h, S. cerevisiae also produced ethanol to a peak concentration of 4 g l -1 .

Monocultures of C. vulgaris in aerated PBR in mixotrophic conditions

C. vulgaris was grown on MBM-GP medium in PBR in the same way as S. cerevisiae in monoculture and as mixed cultures except that the C. vulgaris monocultures were continuously aerated. Aeration was mandatory for CO2 provision to C. vulgaris for photosynthesis. The pO2 in the culture was expected to be stable at 20.9 % in the absence of net production or consumption of O2 by C. vulgaris.

During the first 48 h of C. vulgaris growth in MBM-GP (Fig. 7a), the glucose and O2 concentrations did not decrease while the population increased slightly from 1×10 6 to 1.8×10 6 cells ml -1 . From 48 to 116 h of incubation, glucose decreased to complete depletion while the microalgae population increased from 1.8×10 6 to 4×10 8 cells ml -1 . During this period, the presumed heterotrophic microalgae growth was exponential with µ=0.09 h -1 ).

Monocultures of C. vulgaris in aerated PBR in photo-autotrophic conditions

C. vulgaris was grown in autotrophic monoculture using MBM medium in the absence of glucose (photoautotrophically) and with continuous aeration (Fig. 7b) to supply atmospheric CO2 as carbon source. The microalgae firstly grew exponentially (µ=0.02 h -1 ), increasing the population from 9×10 5 to 2×10 7 cells ml -1 and producing O2 via photosynthesis. Starting from a value of 21%, the pO2 reached 22% at the end of the exponential growth phase (100 h) then it continued to increase up to 22.3% and remained constant at the same level. This is a significant level of O2 production considering the continuous flow of the air through the photobioreactor and the concentration of cells in the culture.

Impact of ethanol on C. vulgaris growth

The impact of the ethanol produced by S. cerevisiae when grown in MBM-GP medium in photo-bioreactor culture was assessed on C. vulgaris growth. Ethanol was added to C. vulgaris shake-flask cultures when the population reached 7×10 6 cells ml -1 (corresponding to the initial C. vulgaris population in the mixed culture).

Four ethanol concentrations (2, 4, 6 g l -1 and 0 g l -1 ) were chosen according to the range of ethanol concentrations that could be produced by S. cerevisiae in monoculture and mixed culture (Fig. S2). The C.

vulgaris growth profile was the same in all cultures (with or without the addition of ethanol). Moreover, cell viability of the four cultures remained at approximately 98 %.

Mixed cultures of S. cerevisiae and C. vulgaris in closed and non-aerated PBR

Mixed culture No. 1

In the first mixed culture, the yeast inoculum was prepared in the newly designed MBM-GP medium while the microalgae inoculum was prepared in autotrophic MBM medium.

The S. cerevisiae behavior was similar in both mixed culture No. 1 (Fig. 8) and in the reference yeast monoculture (Fig. 6) (same maximum population, same specific growth rate, and same ethanol productivity).

On the other hand, the C. vulgaris growth in mixed culture No. 1 was weak compared with the reference photoautotrophic mixed culture. The microalgae population only slightly increased from 7×10 6 to 9×10 6 cells ml -1 within the first 13 h and remained mainly constant until the end of incubation (168 h) but the dissolved CO2 concentration gradually decreased from 16 % to 0 % from 48 h to 168 h at the end of the experiment.

Mixed culture No. 2

For this second mixed culture, both the yeast and the microalgae inocula were prepared in the MBM-GP medium. This was in contrast to the mixed culture No. 1 where the microalgae inoculum was prepared in autotrophic MBM medium. The mixed cultures No. 2 and No. 1 only differed in the microalgae inoculum preparation. All other conditions including the microalgae:yeast inoculum ratio were identical.

The S. cerevisiae behavior was similar in both mixed cultures (Fig. 9) and in the reference yeast monoculture (Fig. 6) in terms of maximum population, specific growth rate, and ethanol productivity.

C. vulgaris started to grow from the beginning of incubation period, and without a lag phase, until 24 h and reached a maximum population of 2.4×10 7 cells ml -1 , then its population remained stable until the end of the experiment.

CO2 production by S. cerevisiae in monoculture

In monoculture of S. cerevisiae using MBM-GP medium, yeast biomass, ethanol and CO2 were produced during growth, the latter resulting in the acidification of the culture medium. Since a stable pH was specified for the fermentation, the acidification of the culture resulted in the automatic addition of KOH in step with yeast growth during the first 41 h of the culture. Ethanol (3.95 g l -1 ) was produced (Fig. 6) and CO2 (3.91 g l -1 ) was released. The CO2 concentration was calculated by adopting the stoichiometric fermentation equation 2 [START_REF] Verduyn | Physiology of Saccharomyces cerevisiae in Anaerobic Glucose-Limited Chemostat Cultures[END_REF]) using the ethanol yield (3.95 g l -1 ) of the yeast monoculture in MBM-GP medium:

8.98 C6H12O6 + 0.63 NH3  1 C3.75H6.6N0.63O2.1 + 15.4 C2H6O + 16 CO2 + 1.1C3H8O3 + 0.8 H2O

(2)

The CO2 released into the culture medium reacts with water to form carbonic acid H2CO3 and then dissociates into H + and HCO3 - [START_REF] Peña | Effects of high medium pH on growth, metabolism and transport in Saccharomyces cerevisiae[END_REF] acidifying the culture medium. Under the pH-control regime, the KOH solution is added to maintain the pH at 6.5. The stoichiometry of the reaction between CO2 and KOH is 1:1. A total KOH volume of 337 ml was added during the yeast growth phase, which corresponded to 0.337 mole of KOH added to the 5-liter culture medium. For ease of the mass balance calculation, the amount of KOH added was expressed as a concentration (6.74×10 -2 mol l -1 ):

[KOH] = VKOH × C KOH V (3)
with:

[KOH]: base KOH concentration in the culture medium (mol l -1 ) VKOH: volume of KOH added to the culture medium (l)

CKOH: concentration of the KOH solution added to the photo-bioreactor (mol l -1 )

V: working volume (5 l)

Assuming that the KOH reacted exclusively with the H + from the hydration of the CO2 produced, 6.74×10 -2 mol l -1 of KOH was used for pH adjustment:

CO2 + H2O  H2CO3  H + + HCO3 - (4)
The CO2 concentration produced by yeast and neutralized by the KOH was 2.97 g l -1 and was calculated as:

[CO2]KOH = [KOH] ×MCO2 (5)
with:

[CO2]KOH: concentration of CO2 produced by yeast and reacted with KOH (g l -1 )

[KOH]: base KOH concentration in the culture medium (mol l -1 ) MCO2: molar mass of CO2 (44 g mol -1 )

From the above calculation, 3.91 g l -1 of CO2 would have been produced during yeast monoculture but only 2.97 g l -1 of CO2 was measured based on the KOH used. This means that 0.94 g l -1 of CO2 remained in solution and/or passed into gaseous phase (Fig. 10b).

CO2 mass balance for S. cerevisiae and C. vulgaris in mixed culture No. 2

The CO2 production and biofixation was studied only in mixed culture No. 2 since the dominance between microalgae and yeast was reached in this mixed culture and not in mixed culture No. 1.

In mixed culture No. 2, the KOH solution was added during the first 39 h of culture corresponding to yeast growth. As explained above, the S. cerevisiae behavior was similar in both mixed culture No. 2 and in the reference yeast monoculture (Fig. 10a); again the assumption was made that the KOH solution was mainly added to the mixed culture No. 2 to compensate for the medium acidification by the CO2 release by the yeast.

KOH (283 ml) was added during the growth phase of the yeast corresponding to 5.7×10 -2 mol l -1 of CO2 equivalent to 2.49 g l -1 of CO2 (equation 3 and 5).

In the yeast reference monoculture 2.97 g l -1 of CO2 reacted with KOH whereas in mixed culture No. 2 only 2.49 g l -1 , of CO2 reacted with KOH. The difference in CO2 concentration most likely corresponds to the amount of CO2 assimilated by microalgae in the mixed culture: 0.48 g l -1 of CO2 i.e. 0.13 g l -1 of carbon. This concentration of carbon is coherent with the concentration of carbon required for the C. vulgaris biomass measured in mixed culture No. 2; 1.5×10 7 cells ml -1 of C. vulgaris was produced corresponding to a dry weight of 0.23 g l -1 or 8.8×10 -3 mol l -1 (the microalgae composition is C1H1.78N0.165O0.495 according to [START_REF] Scherholz | Achieving pH control in microalgal cultures through fed-batch addition of stoichiometrically-balanced growth media[END_REF], and consequently 0.11 g l -1 of carbon was required for the microalgae biomass production. Hence, the amount of carbon fixed by microalgae was determined by two different methods; the carbon fixation by C. vulgaris calculated from the microalgae biomass concentration corresponded to 85% of that calculated from the KOH consumption.

Discussion

The aim of this work was to establish a symbiotic relationship between a heterotroph organism and an autotroph organism based on gaseous (CO2) exchange. For this relationship to be useful in terms of in situ CO2 mitigation, the rates for CO2 production and consumption must be equal and in order to achieve this neither organisms must dominate, hence a co-dominant culture is necessary.

One of the main challenges in developing a mixed culture of a heterotroph and an autotroph is the selection of medium that would allow the co-dominance of the two species. As the µ of C. vulgaris is considerably smaller (slower) than that of S. cerevisiae, the growth medium was designed to favor C. vulgaris development and to limit S. cerevisiae growth. The MBM-GP medium was a good compromise for a co-dominant culture of C. vulgaris and S. cerevisiae (Fig. 5). Both organisms were able to grow in this medium based on the available nitrogen and carbon for both C. vulgaris and S. cerevisiae: C. vulgaris could obtain nitrogen from nitrate, both organisms could access the short peptides and amino acids in the peptone and S. cerevisiae could additionally use the NH3 supplied by the peptone. Glucose as carbon source would be available to both organisms but CO2 would be additionally available to C. vulgaris. S. cerevisiae growth was limited by the availability of assimilable nitrogen to this organism. S. cerevisiae did not grow in the absence of peptone, as was the case with MBM-G medium. The addition of yeast extract to MBM-G medium to give MBM-GY medium provided a nitrogen source as well as other nutrients that could be used by the yeast for growth, however, the addition of yeast extract to the growth medium proved toxic to C. vulgaris. Finally, the MBM-GP medium allowed growth of both yeast and microalgae and also compensated for the higher µ of yeast by limiting the maximum yeast population at 10% of the microalgae population.

Having designed a medium suitable for the growth of both organisms, three reference cultures were grown in photo-bioreactor: a fermentative culture of S. cerevisiae without aeration (MBM-GP medium), a culture of C.

vulgaris under mixotrophic conditions with continuous aeration (MBM-GP medium) and an autotrophic culture of C. vulgaris with continuous aeration and in the absence of glucose (MBM medium). Two mixed cultures were also grown without aeration in MBM-GP medium.

The glucose was mainly fermented by S. cerevisiae in monoculture (Fig. 6) although the possibility of some respiration cannot be ruled out. S. cerevisiae mixes respiration and fermentation in the presence of O2 and when external glucose concentration exceeds 0.8 mmol l -1 (0.1 g l -1 ) [START_REF] Verduyn | Continuous measurement of ethanol production by aerobic yeast suspensions with an enzyme electrode[END_REF][START_REF] Otterstedt | Switching the mode of metabolism in the yeast Saccharomyces cerevisiae[END_REF]). This phenomenon is called the "Crabtree effect" [START_REF] Verduyn | Continuous measurement of ethanol production by aerobic yeast suspensions with an enzyme electrode[END_REF].

Under mixotrophic conditions C. vulgaris in monoculture (Fig. 7a), grew without using glucose and O2 at the beginning of the culture, which, indicated photoautotrophic growth of the organism also reported by Ben [START_REF] Amor-Ben Ayed | The Use of Chlorella vulgaris to Accumulate Magnesium under Different Culture Conditions[END_REF]. After 48 of incubation, C. vulgaris started to grow heterotrophically using glucose and O2. C. vulgaris seems to "privilege" autotrophy as long as the microalgae population is small enough to allow satisfactory light penetration into the PBR. After that, C. vulgaris seems to have, at least, partly switched to heterotrophic metabolism. Microbial growth leads to an increase in light absorption and auto-shading by the microorganisms [START_REF] Pfaffinger | Model-based optimization of microalgae areal productivity in flat-plate gas-lift photobioreactors[END_REF]. The population in the shaded volume (central section of the PBR) may have used glucose and O2 for growth through respiration, while the population in the lit volume (at the edge of the PBR and closed to the light source) could have grown photoautotrophically. In a well-mixed culture, as employed in this study, this means that as the average amount of light available to each cell decreases, C. vulgaris increasingly progresses towards a more heterotrophic metabolism.

In mixed culture, S. cerevisiae with its higher µ could be expected to rapidly consume all glucose before C.

vulgaris would have time to grow heterotrophically. This means that the latter would grow fully photoautotrophically in mixed culture. For this reason, a reference culture of C. vulgaris was grown under photoautotrophic conditions in the usual growth medium used for this purpose; MBM medium (Fig. 7b).

For the C. vulgaris photoautotrophic monoculture the exponential growth phase was followed by a longer linear growth phase from the point where the population density increased beyond 2×10 7 cells ml -1 . This is most likely due to light limitation; once the culture reaches a certain population density that would result in considerable autoshadowing and restricted light penetration into the core of the culture. The growth would then be directly related to the light arrival rate, which is constant resulting in linear growth.

In summary, the S. cerevisiae monoculture (Fig. 6) and the microalgae C. vulgaris autotrophic monoculture (Fig. 7b) were used as reference cultures to compare with the mixed cultures. The respective µ were used to adjust the microalgae:yeast inoculation ratio to 30:1 (equation 1) in an attempt to minimize the possibility of yeast domination in the mixed culture. Two mixed cultures were grown without aeration where an attempt was made to coordinate the growth of the two organisms by adjusting their respective inoculation rates as described above.

In the first mixed culture (No. 1) (Fig. 8), S. cerevisiae consumed the glucose within the first 48 h as had been observed in the reference yeast monoculture (Fig. 6). The S. cerevisiae biomass and ethanol production were the same for both the mixed culture and the yeast reference culture. Since no glucose was available for the microalgae, C. vulgaris probably grew fully photosynthetically in the mixed culture. This was additionally supported by the observation that in the C. vulgaris monoculture in the presence of glucose, the glucose was not consumed during the first 48 h (Fig. 7a). On the other hand, C. vulgaris growth in mixed culture was weak with the maximal microalgae population 2.5 times lower than the reference monoculture (9×10 6 and 2×10 7 cells ml -1 respectively) (Fig. 7b). Ethanol or CO2 toxicity can both be excluded as reasons for this low microalgal biomass production, as discussed below. Although the C. vulgaris population was weak, the microalgal cells remained active during the entire experiment (168 h). During the latter phases of the experiment, there were instances where the sun shone directly on the PBR; intermittent negative pCO2 troughs and concomitant positive pO2 peaks were observed during these transient periods. This can be taken as a strong indicator that both organisms in the mixed culture were metabolically active and that synergy effects between yeast and microalgae occurred. The final pCO2 concentration reached almost its initial level indicating that in principle, in situ CO2 mitigation in mixed culture is feasible, although the efficiency of the process remains to be improved.

A second mixed culture (No. 2) was grown to increase the microalga population in mixed culture (Fig. 9). The C. vulgaris inoculum was prepared in the same medium as used for the mixed culture (MBM-GP) in order to pre-adapt the organism to this medium and promote immediate growth of C. vulgaris straight after inoculation into the photo-bioreactor. C. vulgaris grew straight away from the start reaching a maximum population 2.7 times higher than that of the first mixed culture. By modifying the preparation of the inoculum, it was possible to achieve the same population concentration for both organisms (2×10 7 cells ml -1 ). Presumably the pre-adaptation allowed C. vulgaris to have the enzymes necessary for the utilization of the amino acids and the small peptides present in the peptone in the photo-bioreactor medium. Like in the first mixed culture, C.

vulgaris grew on the CO2 produced by S. cerevisiae as there was no other source of CO2. Of the CO2 produced by S. cerevisiae in mixed culture No. 2, 12% was consumed directly by C. vulgaris, and the 64% of CO2 captured by the KOH was in the HCO3 -form and still available to the microalgae for utilization (Fig. 10c).

Ethanol is known to exhibit antimicrobial activity by attacking cell membranes [START_REF] Patra | Under the influence of alcohol: The effect of ethanol and methanol on lipid bilayers[END_REF]) and it was important to assess the potential toxicity of the ethanol produced by S. cerevisiae in mixed culture on C.

vulgaris. Firstly, the growth of the microalgae in the mixed culture No.2 provided the first indication that at 4 g l -1 , ethanol was not toxic to C. vulgaris. This observation was further confirmed with a shake-flask experiment were exogenous ethanol (2, 4 and 6 g l -1 ) was added to growing autotrophic cultures of C.

vulgaris. The same growth profiles were observed for both control cultures and cultures containing ethanol, even at concentrations higher than those measured in mixed cultures. The cell viabilities of these monocultures were high (approx. 100%) even at the end of the incubation period (411 h).

In conclusion, in order to encourage mutual symbiosis, we developed a mixed culture of C. vulgaris and S.

cerevisiae in PBR in a way that neither organism dominated the other in terms of population concentration.

The method developed for simultaneous cell enumeration with flow cytometry permitted to rigorously monitor the two populations in the mixed culture. The dissolved O2 and CO2 probes brought relevant measurements that allowed us to follow gas evolution. The results indicated that the medium design, the culture conditions, the inoculum ratio and the C. vulgaris inoculum preparation all contributed for codominance of the two species. By comparing the physiological behavior of microalgae and yeast in monoculture and mixed culture, co-dominance and a mutual symbiosis based on in situ gas exchange were demonstrated. This work opens the perspective for in situ CO2 mitigation, full utilization of the organic substrate and a reduction in aeration costs of biotransformation processes. The culture was not aerated, and the bioreactor was fitted with a fermentation lock, by using a U-tube manometer filled with 200 mm of H2O. The pO2 was monitored continuously using the immersed optic pO2 probe and the pCO2 sensors was placed in a loop that passed culture over it continuously by means of a peristaltic pump

Figure captions

Fig. 1 Fig. 2 Fig. 3

 123 Fig. 1 A coupled culture (a) and a mixed culture (b) of heterotrophic and autotrophic organisms; (a) gases pass from the liquid phase of the heterotrophic culture into a gaseous phase (blue dashed arrows) and they then pass from the gaseous phase into the liquid phase of the photo-bioreactor (red solid arrows); (b) Diagram of a mixed culture of heterotrophic and autotrophic organisms with CO2 produced by heterotrophic metabolism (the gases are generated and reused in situ)

Fig. 4 Fig. 5 Fig. 8 Fig. 9 Figures

 4589 Fig. 4 Validation of simultaneous enumeration of C. vulgaris and S. cerevisiae by flow cytometer; experimental versus reference ratios of C. vulgaris in eleven mixed cells suspensions (black circles) at different microalgae:yeast ratios

Acknowledgements

The authors would like to thank Département de la Marne, Région Grand Est, and Grand Reims for their financial support.

Compliance with ethical standards

 Conflict of Interest Statement

The authors declare no conflict of interest.

 Statement of Informed Consent, Human/Animal Rights This article does not contain any studies with human participants or animals performed by any of the authors.

 Declaration of authors agreement

The authors declare their agreement to authorship and submission of the manuscript for peer review.


Gabriel Marzinotto

Jeremy Auguste

Frederic Bechet

Geraldine Damnati
email: geraldine.damnati@orange.comjeremy.auguste

Alexis Nasr
email: alexis.nasr@lis-lab.fr

Semantic Frame Parsing for Information Extraction : the CALOR corpus

Keywords: Frame Semantic Parsing, LSTM, CRF

This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The main difference in our approach compared to previous works on semantic parsing with FrameNet is that we are not interested here in full text parsing but rather on partial parsing. The goal is to select from the FrameNet resources the minimal set of frames that are going to be useful for the applicative framework targeted, in our case Information Extraction from encyclopedic documents. Such an approach leverages the manual annotation of larger corpora than those obtained through full text parsing and therefore opens the door to alternative methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark corpus. The approaches compared in this study rely on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The models compared are CRFs and multitasks bi-LSTMs.

Introduction

Semantic Frame parsing is a Natural Language Understanding task that involves detecting in a sentence an event or a scenario, called Frame, as well as all the elements or roles that can be associated to this event in the sentence, called Frame Elements. One of the most popular semantic frame model is the Berkeley FrameNet project developed by ICSI Berkeley [START_REF] Baker | The berkeley framenet project[END_REF]. This model is composed of an inventory of Frames with, for each of them, a list of words, called Lexical Units (or LU), that can trigger a frame in a sentence. Besides, for each frame, a list of Frame Elements (FE), core or optional, is defined. LUs are pairings of a word with a sense; Frame Elements are the components of a frame, represented by sequences of words in a sentence. Two kinds of parsing can be done with a Semantic Frame model: full text parsing where each word in a sentence is analyzed to check if it can trigger a frame; and partial parsing where only a subset of frames and LUs is considered, according to their relevance for a given applicative framework. Annotating all the possible LUs and frames in a sentence is a very difficult (and expensive) task for human annotators, therefore there are very few corpora annotated this way. Moreover not many languages have such resources. Most of previous work in semantic frame parsing that has been done with a full text parsing approach have used the benchmark corpus FrameNet 1.5. Although the size of this benchmark is relatively large, a lot of frames have a very small number of occurrences in the corpus. This is due to the very large number of frames considered in the semantic model and this makes this corpus particularly challenging for machine learning methods. On the contrary partial parsing can be made on large corpora at a reasonable cost: because the amount of frames and LUs is limited, the annotators can focus only on a few words for each sentence, making the task much easier than full parsing. Corpus obtained this way contain much more examples for each frame, opening the door to more machine learning methods than it is the case with full parsing.

From an applicative point of view, partial parsing is also a more realistic option. Although the different senses from the FrameNet model are generic, models trained on the FrameNet 1.5. corpus are not. Assuming that a full text annotation will be available for each new applicative domain is not an option. Moreover a lot of applicative frameworks using semantic models such as frames, like the Information Extraction framework considered in this study, are not interested in full parses but on the contrary only in some specific senses related to the domain targeted. An example of this kind of annotation scheme is given in figure 1. As we can see, only two words as considered as lexical units in the sentence: decide, which triggers the frame Deciding and order triggering the frame Request. This paper presents a study on the use of Sequence Labeling models such as CRF and LSTM for Semantic Frame parsing. Unlike previous studies developing a multi-step approach involving first the Frame identification task, then the frame element detection and labeling tasks, we propose an approach detecting simultaneously LUs and Frame Elements making Frame identification and argument selection an integrated process. A simple heuristic filter is used in order to maintain coherence in the hypotheses produced. We compare two popular sequence labeling methods: conditional random fields and recurrent deep neural networks using Long Short-term Memory.

The CALOR-Frame corpus

We introduce in this paper the corpus CALOR, which is a collection of documents in French language that were hand annotated in frame semantics. This 1.3M words corpus contains documents from 4 different sources: Wikipedia's Archeology portal (WA, 201 documents), Wikipedia's World War 1 portal (WGM, 335 documents), Vikidia's portals of Prehistory and Antiquity (VKH, 183 documents) and ClioTexte's 1 resources about World War One (WW1) Figure 1: Example of Frame multilabel annotation (CTGM, 16 documents). These sources were chosen to guarantee both writing style and domain diversity. By having documents from Vikidia (an encyclopedia addressed to children from 8-13 years old) and Wikipedia, presenting subjects of ancient history and archeology we can compare the influence of the writing style on the complexity of the task. The same analysis is possible on the WW1 documents, as Cliotexte (a collection of historical documents such as letters, essays, speeches from WW1) and Wikipedia share a common domain with a completely different writing style. This document selection allows to study the importance of the nature of the training data on the performance of the system on a test set on the same subject. Moreover, having data from two different portals of Wikipedia allows to study the domain dependency problem. For example: evaluate if a model for a frame F, trained on data from the archeology domain can successfully be applied on data from the WW1.

In contrast to full text parsing corpus, the frame semantic annotations of CALOR are limited to a small subset of frames from FrameNet [START_REF] Baker | The berkeley framenet project[END_REF]. As described in the introduction, the goal of this partial parsing process is to obtain, at a relatively low cost, a large corpus annotated with frames corresponding to a given applicative context. In our case this applicative context is Information Extraction (IE) from encyclopedic texts, mainly historical texts.

To this purpose we extracted from our 1.3M word text corpus the top 100 most frequent verbs, then we kept those that were more likely to correspond to an action or a situation that would be relevant in our IE context. For example, verbs such as discover or build are very relevant for exploring archaeological documents. We looked for the corresponding frames of the selected verbs in the Berkeley FrameNet lexicon and kept a set of 53 different frames. If a verb could trigger several frames, we only kept those which were relevant in our corpus.

By adding noun triggers to the list of selected verb triggers we obtain a list of 145 Lexical Units (LU), with 30,950 occurrences in the training corpus. Selecting the most frequent verbs and nouns from the 1.3M words CALOR corpus as frame triggers is a guarantee that the average number of occurrences per frame is high. Therefore, even if the list of frames annotated in the CALOR corpus is small compared to the Framenet corpus, we have a large variety of occurrences for each of them, allowing us to build robust parsers for encyclopedic texts, which is the goal of the CALOR corpus. The list of Frames in CALOR is provided in Table 1.

The annotation process

Once the corpus, the lexical units and the frame set were chosen, we developed an iterative process for the manual annotation of the CALOR corpus. Preliminarily to this annotation process, the documents were automatically processed by the Macaon [START_REF] Nasr | Macaon: An nlp tool suite for processing word lattices[END_REF] tool suite (sentence segmentation, tokenization, POS Tagging, lemmatization, and dependency parsing). Every word within the documents which lemma belongs to the set of selected LUs generates an example to be annotated. It is possible that one sentence generate several examples to annotate if it contains several LUs. We obtained a set of 30,950 examples to annotate, corresponding to all the LUs occurrences in the CALOR corpus. Three annotators were hired for this project. Their goal was to process these 30,950 examples: decide for each of them if its corresponding LU triggers or not one of the 53 frames selected, and finally annotate, if a frame was triggered, all its Frame Elements (FE) occurring in the sentence. In order to reduce the manual annotation time and perform quality control on the corpus produced, we designed an iterative process based on three principles:

• an automatic pre-annotation scheme based on the frame parser that will be presented in section 4.1.;

• a batch selection process that selects from the unlabeled corpus a set of examples to annotate that corresponds to the same LU in very similar syntactic and lexical contexts.

• an automatic quality control estimator that regularly retrains the frame parser and evaluate its performance thanks to a k-fold experiments on the part of the corpus already manually annotated.

The iterative process based on these principles can be implemented as follows:

1. Frame pre-annotation parsing : an automatic frame parsing process is applied to each example to annotate. It predicts the frame label and the possible FEs for the LU contained in the example. All these automatic annotations are manually checked and eventually corrected by our annotators. At each iteration the frame parser (see section 4.1.) is trained on the subset of the CALOR corpus that is already annotated.

For the first iteration, since there is no data to train the parser, all the LUs are labeled with a "no frame" label. Each iteration brings more data to train the frame parser.

4.

Model training and quality control validation : at each iteration, the frame parsing models are re-trained on the corpus of manually processed examples. A k-fold evaluation is also performed to monitor the evolution of the parsing performance of the model when more validated data is added to the training corpus. If the frame parsing performance improves, it is a good indication that the added data is coherent with the annotations already processed in the previous iterations. This can be seen as a quality control measure of the annotation process on the whole dataset, in addition to inter-annotators agreement measures than can also be estimated on small subsets of the corpus.

Corpus Statistics

Comparison with other corpora

The comparison between the CALOR-Frame corpus and other corpora with semantic frame annotations is given in table 3. The column Documents shows the main sources of documents annotated, # Sent counts the number of sentences in each corpus; % Sent. w/Frame shows the percentage of sentences that have a Frame annotation; Word Lexicon displays the size of the lexicon of each corpus; Frame lexicon, LU lexicon and FE lexicon correspond to the number of frames, LUs and FEs considered in the annotation model; finally, # Frame occurrences shows the number of Frames annotated in the whole corpus.

As we can see, the CALOR-Frame corpus is the only corpus that is not oriented towards journalism, news or current events, it is also the corpus with the largest lexicon size.

When we compare it to the existing dataset for Semantic Frame Parsing in English (SemEval07) and French (AS-FALDA) [START_REF] Candito | Developing a french framenet: Methodology and first results[END_REF] we observe that the CALOR corpus has the smallest frame lexicon, but the biggest number of annotations.

Frame parsing as a sequence labeling task

As mentioned in the introduction, semantic frame parsing is a structured prediction task where a word can belong to several structures. For example, in figure 1, the word general can belong to both the frame Request as the frame element Speaker and the frame Deciding as the Cognizer.

In order to consider the Frame parsing task as a word sequence labeling task, we need to flatten the frame structures by labeling each word with both semantic and information structure. In this study we have decided to use the simple 1. Multi-labels: each word can receive more than one label according to the number of frames occurring in a sentence. Each label contains the frame as well as the frame element identifiers, therefore there are too many labels to consider building complex labels combining all of them.

2. Linking: an explicit link to the LU triggering a frame is added to each word label of its FEs, as can be seen in table 4. This information is necessary as several expressions of the same frame can occur in a sentence triggered by several LUs. The absolute value of these links is not meaningful and cannot be predicted the same way as the semantic labels are.

In this study we compare two different strategies in order to deal with the multi-label issue, one based on CRF with a multi-model approach (each LU has its own prediction model) and one based on a bi-LSTM model following a multi-task approach. They are described in the next section.

Sequence labeling models

Multi-model CRF approach

CRF-based approaches have been used in many NLP tasks involving sequence labeling such as POS tagging, chunking or named entity recognition [START_REF] Mccallum | Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons[END_REF]. In order to apply CRF to frame parsing, as described in section 3., we need to address the multi-label issue. Since we want to perform frame disambiguation and semantic role detection in one step, and because each word in a sentence cannot trigger more than one frame, we chose a multimodel approach where a CRF-model is trained for each word belonging to the LU lexicon. This approach is described in Figure 3. At training time, the corpus is split according to the LU lexicon: to each word W i belonging to this lexicon is attached a sub-corpus containing all the sentences C Wi where W i occurs. For each sentence s ∈ C Wi , W i can trigger a frame F among all the possible frames for this word in the LU lexicon, or nothing.

Multi-task LSTM approach

Deep Neural Networks (DNN) with word embedding is the state of the art approach for semantic frame parsing [START_REF] Hermann | Semantic frame identification with distributed word representations[END_REF]. More recently recurrent neural networks (RNN) with Long Short Memory (LSTM) cells have been applied to several semantic tagging tasks such as slot filling [START_REF] Mesnil | Using recurrent neural networks for slot filling in spoken language understanding[END_REF] or even frame parsing [START_REF] Hakkani-Tür | Multidomain joint semantic frame parsing using bi-directional rnn-lstm[END_REF][START_REF] Tafforeau | Joint syntactic and semantic analysis with a multitask deep learning framework for spoken language understanding[END_REF] for Spoken Language Understanding. Following these previous works, we propose in this study a single-layered bidirectional LSTM sequence to sequence architecture to perform frame tagging. To deal with the multi-label issue we could train a biLSTM model per LU, using the same approach as for the CRF, however, the number of examples per LU is reduced and neural networks do not perform well on small datasets. We would face the same problem if instead we decided to train one biLSTM per frame. The remaining possibility is to build a single biL-STM to predict all possible frames in order to automatically learn a feature representation meaningful to all frames. We chose a multi-task approach (biLSTM-MT) similar to the one proposed by [START_REF] Tafforeau | Joint syntactic and semantic analysis with a multitask deep learning framework for spoken language understanding[END_REF], which models each frame as an isolated task. In this model two LSTM models, one forward and one backward are concatenated and shared among all tasks. Then a task-specific fullyconnected output layer is added for each task.

In this work we consider each frame of our FrameNet model as a different task. This approach is described in figure 3. At decoding time each sentence is processed by the network and a distribution probability on the labels of each task for each word is produced. We only keep the labels above a certain threshold.

Coherence filter

Once the sequence tagging process is performed, each word is labeled with a frame element and position labels or a null label (O) as presented in table 4. Because the labels given at the word level might not be coherent at the frame level, we apply a coherence filter to the output of the tagging process. This filter is in charge of removing incoherences (FEs not starting with a B label; FEs without a frame) and linking the FEs to the LU that triggered the corresponding frame. This filter implement is a very simple strategy: in a given sentence, if a word W of index i is labeled as a LU trigger for frame F , we link all the FEs detected in the sentence with the same frame label F to the LU w i . At the end of this process, all FEs that have not been linked to a LU are removed.

Feature selection

The feature sets used for the CRF and the biLSTM approaches differ. For the CRF model, each training sample contains only one trigger word which is clearly identified, therefore we can use this information in order to add global constraints on the feature set of each word to process. On the opposite the multi-task biLSTM models can process several triggers in the same sentence, therefore the feature set cannot be biased toward a specific trigger and only local features are considered.

For the CRF models we consider 3 features: word lemma, part-of-speech (POS) and the syntax dependency path between the word to process and the potential frame trigger in the sentence. This dependency path is built through the concatenation of the syntactic functions between the word and the trigger. In the general case, a trigger is not necessarily at the root of the syntactic tree, for this reason, the dependency paths are composed of both links from child to parent (ascending links) and from parent to child (descending links) we make distinction of both types of links in the way we encode the dependency path.

For the biLSTM-MT we consider 4 features: word embeddings (Glove embeddings of 200 dimensions trained on French Wikipedia), POS, syntactic function (without a link) and a boolean indicator of whether the word belongs to the lexicon LU or not. All the features are encoded as trainable embeddings, we allow the network to adapt them during the training of the frame parsing task.

For both systems, in order to extract lemmas, POS and syntactic dependency trees we processed the frame annotated corpus using MACAON [START_REF] Nasr | Macaon : Une chaine linguistique pour le traitement de graphes de mots[END_REF] trained with a set of POS and dependencies similar to the one proposed in the Paris French TreeBank [START_REF] Abeillé | Building a treebank for french[END_REF][START_REF] Abeillé | Enriching a french treebank[END_REF]. The main differences between the multi-model CRF and multi-task biLSTM models are summarized in table 5.

Evaluation

Experimental setup

When a sentence is processed, there are 4 steps or subtasks that take place either explicitly or implicitly in the frame parsing process. Even though our approaches perform frame detection and semantic role labeling in one step, we detail the scores for each sub-task because they are relevant indicators of the performance of a parser, and they serve as point of comparison between our models. These 4 sub-task are:

1. trigger identification (TI) which decides whether a word in a sentence can trigger a frame or not;

2. trigger classification (TC) which assigns the frame label to the trigger word detected to form a LU;

3. role filler identification (RI) which detects potential semantic role fillers in the sentence for the frame detected;

4. role filler classification (RC) which assigns a label to each role filler detected in order to obtain the Frame Elements (FE) of the frame detected.

In this study we consider these 4 tasks as a cascade process: an error in task 1 will lead to several errors in task 4 since no correct FEs will be detected if the frame is not triggered.

The CALOR corpus is annotated with a small number of frames compared to the FrameNet 1.5 corpus, therefore the ambiguity for subtasks 1 and 2 is rather small. The most complex task is of course task 4, role filler classification, since every detection and classification of LUs and FEs has to be correct. That is why we will pay a special attention to this task to compare the models in the following experiments.

To carry out our experiments we divide the corpus CALOR assigning 80% of the frame occurrences to the train set and 20% to the test set. This split is done in such a way that the frame distribution remains as similar as possible between train and test while considering a document as an atomic unit that cannot be subdivided and should be either in the train or in the test set. This split does not take into account the LU distribution, a LU can therefore appear both in train and test, only in train or only in test.

Similarly to previous work on semantic frame parsing we will use precision, recall and f-measure on the 4 subtasks presented in order to evaluate our approaches. We also compute precision/recall curves by using different acceptation thresholds on the frame and semantic role hypotheses output by our models. In this study we set the operating point for comparing our models to the Equal Error Rate (EER) between the precision and recall measures.

Overall results

The comparison between the multi-model CRF (CRF-MM) and multi-task biLSTM (biLSTM-MT) performance over the set of 4 sub-tasks presented in the previous section is presented in Table 6. Both systems use their full feature set as presented in table 5 with EER between precision and recall as the chosen operating point. As expected performance for subtasks 1 and 2 are very good, much better than those reported on the FrameNet 1.5 corpus [START_REF] Hermann | Semantic frame identification with distributed word representations[END_REF]. This is due to the partial parsing approach used here where only a subset of the FrameNet model is used. CRF-MM model performs better on the trigger identification and classification tasks (subtasks 1 and 2) while the biLSTM-MT model is better on role identification and classification (subtasks 3 and 4). The reason for this behavior is that it is easier for a CRF model to identify the proper frame for each trigger word, as they can only trigger a few frames, while for the biLSTM-MT all frames are in competition. This reduction of ambiguity for CRF-MM models has a cost: splitting the training corpus according to the LU lexicon and therefore reducing the training data for each model. If this is not an issue for the two low ambiguity subtasks 1 and 2, the situation is different for subtasks 3 and 4. Indeed biLSTM-MT is a single model that learns from all training data, it is able to automatically learn relevant features and capture semantic aspects of text using a shared layer and then use these features to classify tokens in roles of different frames learned one by tasks. This ability to use the whole corpus leads the biLSTM-MT model to outperform CRF-MM for tasks 3 and 4. This result is confirmed by figure 4 which displays the precision/recall curves of both methods on subtask 4. As we can see, biLSTM-MT is better in terms of maximal Fmeasure. It is interesting to observe that both models do not show the same precision-recall trade-off. The CRF-MM is able to parse text at a high precision with a low recall, this is not the case of biLSTM-MT, that handles a much larger set of frames and is more prone to precision errors. On the other hand, biLSTM-MT achieves a better recall at a fairly good precision, this happens because it is trained on the full dataset and it is able to learn syntactic patterns from different frames and extrapolate them from one frame to the other.

Conclusion

In this paper we introduced the CALOR corpus as well as a comparison between two different semantic frame parsing Table 6: Comparison of our two models on their full feature set operating at EER models that consider the task as a sequence labeling task.

The main contribution of this work is to propose a new corpus, publicly available, that contain semantic frame annotations that differ from previous corpus such as FrameNet and SemEval. In our case only partial annotation is considered, allowing to annotate much larger corpora at a lower cost than full text annotation. Only a small subset of the FrameNet lexicon is used, however the amount of data annotated for each frame is much larger than in other corpora, allowing to develop and test different parsing methods.

In this study two frame parsing models are compared, one based on CRF following a multi-model approach and other based on biLSTM with a multi-task approach. Experiments show that biLSTM-MT model achieves a better recall, while CRF-MM achieves better precision, this is due to the architecture of each model, in CRF-MM we divide frame parsing into small subtasks one per LU, reducing the number of possible labels in each decision, thus augmenting the precision. On the other hand biLSTM-MT is able to share data across LUs boosting its capacity to deal with complex syntactic patterns and being able to retrieve more frame elements during parsing.

Figure 2 :

 2 Figure 2: Distribution of the frame occurrences in the CALOR-Frame corpus

Figure 3 :

 3 Figure 3: Two different strategies (CRF and bi-LSTM multitask) for Frame parsing

Figure 4 :

 4 Figure 4: Precision Recall curves for CRL-LU and biLSTM-MT on the full feature set Fmeasure CRF-MM Fmeasure biLSTM-MT Trigger Ident. (TI) 96.4 95.5 Trigger Classif. (TC) 95.2 92.3 Role Ident. (RI) 69.7 70.3 Role Classif. (RC) 60.6 63.2

Table 1 :

 1 List of Frames annotated in the CALOR corpus 2. Batch selection process: the examples that have not yet been manually processed are sorted according to their LUs, then, using a similarity measure taking into account the lexical and syntactic context in which the LUs occur. They are then grouped into batches that will be sent to the annotators for manual validation.

	Accomplishment	Activity-start	Age	Appointing	Arrest
	Arriving	Assistance	Attack	Awareness	Becoming
	Becoming-aware	Buildings	Change-of-leadership	Choosing	Colonization
	Coming-to-believe	Coming-up-with	Conduct	Contacting	Creating
	Death	Deciding	Departing	Dimension	Education-teaching
	Existence	Expressing-publicly	Finish-competition	Giving	Hiding-objects
	Hostile-encounter	Hunting	Inclusion	Ingestion	Installing
	Killing	Leadership	Locating	Losing	Making-arrangements
	Motion	Objective-influence	Origin	Participation	Request
	Scrutiny	Seeking	Sending	Shoot-projectiles	Statement
	Subjective-influence	Using	Verification

the goal here is to reduce the cognitive load of the annotators by splitting the corpus to annotate into small batches sharing very similar properties, likely to be annotated the same way. 3. Manual correction : a GUI allows annotators to work on the batches of examples produced in the previous step. Annotation is done on text only, no syntactic annotation is provided to the annotators. The frame pre-annotations produced by the frame parser are displayed, annotators can correct them and add what is missing.

Table 2 :

 2 Description of the CALOR corpus

Table 3 :

 3 Semantic Frame corpus comparison

	Corpus Name	Annotation Document types # Sent	% Sent w/ Frame	Word Lexicon	Frame lexicon	LU lexicon	FE lexicon	# Frame occurrences
	SemEval07	English Frames		Journals	5946	85.1%	14150	720	3.197	754	23814
	ASFALDA	French Frames	Journals (Le Monde)	21634	60.7%	33955	121	782	140	16167
	CALOR	French Frames/SRL	Wikipedia ClioTexte Vikidia	67283	33.5%	72127	53	145	148	26725
			LU
			lexicon
										CALOR
			CALOR						corpus
			corpus
										Multitask
		C_W1	C_W2	….	C_Wn					corpus
	CRF model training CRF tagging	CRF W1	CRF W2	….	CRF Wn	Frame+FE(W2) Frame+FE(W4) Frame+FE(W8) + +	Bi-LSTM model training Bi-LSTM tagging	sentence S MT bi-LSTM	Task 1 : Frame 1 Task n : Frame n Task 2 : Frame 2 ….
		S_W2	S_W4	S_W8
						coherence				coherence
			sentence S		filter					filter
			LU lexicon		sentence S with frames+FEs				sentence S with frames+FEs

CRF-based strategy with multiple models (1 per word in the LU lexicon)

Bi-LSTM Multi Task strategy (each Frame is a task)

Table 4 :

 4 Example of corpus with B,I,O formatFor example, the sentence shown in table 4 will be duplicated into two sub-corpora, C order with column 3 and C decide with column 4. A CRF model is trained on each C

wi sub-corpus. At decoding time, when processing a sentence S, the same process is applied: first S is duplicated for each word w i of S belonging to the LU lexicon. Then the CRF model corresponding to each w i is applied and the different predictions made by the CRF models are merged. This approach has the advantage of keeping the number of possible labels to predict for each CRF relatively small, limited to the frames that can be triggered by the word considered. Therefore the ambiguity is limited and CRFs can be trained efficiently even with a large number of features. However the drawback is that the training data is split across words in the LU lexicon, therefore similarities among LU are not exploited. This situation is acceptable if enough training examples are provided for each LUs, which is the case for the CALOR corpus.

Table 5 :

 5 Comparative Overview of CRF-LU and biLSTM-MT models

		CRF-LU	biLSTM-MT
	Lexical Feat	Lemma	Glove Word Embeddings
	Morpho-Syntactic Feat	POS	POS Embeddings
	Syntactic Feat	Dep. Path to Trigger	Syntactic Function Embeddings
	Model	145 CRF (1 CRF per LU)	1 biLSTM 54 Taks (1 Task per Frame)
	Scaling	Many LU	Many Taks
	Labeling Process	1 Application per LU per Phrase	Parse all the Frames in 1 Application
	Information Sharing	No Sharing	Implicit Sharing Between Frames

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