Thomas Le Guenan
email: t.leguenan@brgm.fr

Philippe Calcagno

Fernanda Veloso

Virginie Hamm

Annick Loschetter

Camille Maurel

Ferid Seyidov

Philippe Dumas

The H2020 GEORISK Project -Inventory and Assessment of Risks Associated to the Development of Deep Geothermal Heating and Power Projects

Keywords: geothermal development, geothermal heating, geothermal power, risk register, risk assessment, de-risking, insurance scheme

The objective of the GEORISK project in the framework of Horizon 2020, is to facilitate the development of deep geothermal projects (geothermal heating or geothermal power) by developing suitable insurance schemes to mitigate the risks inherent to geothermal drilling. The first step of the project, presented here, is to set-up a risk register and propositions to assess those risks. Based on an extensive literature review, and on the knowledge from the expert of the project, the risk register aims at defining a comprehensive list of the risks associated with the development of deep geothermal projects given the current understanding and feedbacks.

Considering the knowledge at the beginning of a geothermal project, the risk register also highlights the risks that would need mitigation through insurance schemes. The register contains 54 risks which are grouped in 6 categories. On top of listing the risks associated with geothermal drilling, the register will be useful for listing associated technical mitigation measures, considering that the insurance schemes will be needed only if the technical solutions are uneconomical. In the risk analysis step, each risk is then given a score via two usual risk indicators (degree of damage and likelihood of occurrence) and ranked. This step is performed by asking stakeholders to answer a questionnaire. Risk analysis is performed for different type of energy use, different countries and different geological settings. Main results for one group are presented and show that the main risks according to the respondents are mostly related to the uncertainties of the subsurface.

INTRODUCTION

The H2020 project GEORISK aim is to develop new insurance schemes as a mean to mitigate the main risks faced by developers and operators of geothermal energy. In this paper, we focus on the "risk" part of the work, and it consists in answering the following question: "what are the main risks faced by developers and operators of geothermal energy?"

There is already a large literature available regarding risks and geothermal energy (see e.g. appendix I of Le [START_REF] Le Guenan | Risk register. Deliverable number D.2.1 of the GEORISK project[END_REF]). However, for most documents, it focused only on a partial definition of the risks (e.g. induced seismicity risks or environmental risks), or only on a context dependent analysis (i.e. focusing on a particular project or type of geothermal energy). In the GEORISK project, the goal was to take into account most types of risks, with relevance to both types of deep geothermal energy (i.e. district heating and power generation), and taking into account the main types of geothermal plays The project focuses on the countries represented in the GEORISK consortium: Belgium, France, Switzerland, Germany, Poland, Greece and Turkey. However, the risks identified should also be relevant for other European and non-European countries.

The structure of the work is based on the principles of the ISO 31000 norm on risk management. According to the norm, risk assessment is composed of three steps: risk identification, risk analysis and risk evaluation. In this paper, we will present the steps of risk identification and risk analysis. Risk identification consists in listing all the plausible risks for the defined context. In the project, this took the form of building a risk register. The following step, risk analysis, consists in ranking the risks. For this project, we used a qualitative method. We will then present some perspectives following the analysis of the risks and conclude.

BUILDING THE RISK REGISTER

Method

Before risk identification, it is necessary to define the context and the objectives of the whole risk assessment. As stated previously, the objectives here are to identify most of the risks faced in the development of a (deep) geothermal project in Europe. Adopting the point of view of developers or operators of deep geothermal energy, the definition formally used in this assessment is: "the events, or sources of uncertainty, preventing the proper development of the project considering the economic, technical, environmental and social points of view".

The way in which risks are identified depends on the "model" used for their representation. The initial model chosen consisted in a "top event", which was provoked by one or several "causes", and which created various types of "consequences". In addition, each risk defined as the unique combination of a triplet "cause -top event -consequence", could belong to one or several phases in the development of a geothermal project, and one or several risk factors that could modify the severity of the risk. This model of representation quickly turned out to be impractical, generating too many risks, with sometimes only slight differences between different risks. For instance, if a top event like "induced seismicity" has several consequences (e.g. damage to building or local opposition), then it would appear several times in the register.

A second and simpler model was then built, keeping only one field for the description of the risk. Consequently, each risk is described with fewer details, but with a wider range. Some risk descriptions are mostly related to a potential causes, while others are mostly related to types of consequences. This asymmetry in the description was not considered as a problem for the register, and a little level of redundancy was preferred over potential gaps. In addition to the description field, each risk belongs to one or several of the following phases:

exploration and characterization; -drilling; -testing and exploitation; -post-closure.

A "consequence" field is also present in the register but is no longer described in detail. Each risks are tagged with the potential consequence generated: either "economic and performance" or "health, safety and environment" (HSE). It is important to bear in mind that most HSE risks have also an economic consequence due to remediation cost.

The sources used to fill the content of the register are the previous European project such as GEOELEC [START_REF] Fraser | European Geothermal Risk Insurance Fund EGRIF[END_REF], GEOWELL [START_REF] Lohne | Well integrity risk assessment in geothermal wells-Status of today[END_REF], or DARLINGe [START_REF] Nador | D.6.3.1 Manual on the use of the transnational tool-box[END_REF]. This was supplemented by an extensive literature review: see Le [START_REF] Le Guenan | Risk register. Deliverable number D.2.1 of the GEORISK project[END_REF] for the full list of references. Finally, a workshop with members of the Georisk project helped to finalize the risk register.

Results

The "External hazards" include only 2 risks: one is a natural hazard damaging the infrastructure and the other is from anthropogenic hazard.

The risks due to uncertainties in the external context are mainly related to the socio-economic environment: some favorable factors are needed in order to make development of geothermal energy possible, so risks depend on the existence or not of these factors.

Risks due to internal deficiencies are related to several types of factors, which are in the area of responsibility of the operator or developer, which could create unwanted consequences. Examples are lack of expertise or risks due to human errors.

Risks due to subsurface uncertainties are the main focus of the GEORISK project. They include the geological and geothermal resource hazards for new drilling projects during drilling phase, also identified as the "short-term" risk and during exploitation for the "long-term" risk. In the register, the "resource" is detailed by most of its parameters, including temperature, flow rate, initial pressure, water composition, etc.

"Technical issues" include risks that appear from operational problems, mainly during the drilling phase, also including failure of material.

Environmental risks include for instance leakage, induced seismicity and ground movements.

These categories were mainly created for ease of use and communication purposes, as some risks could belong to several categories. The risk register was then communicated to various stakeholders in each country of the consortium, which allowed to get valuable feedbacks.

Discussion

Overall, the stakeholders appreciated the risk register, and highlighted only minor gaps, meaning that the list is close to be comprehensive. The main remarks were not on the content of the register but on some conclusions related to the establishment of a list of risks.

Stakeholders expressed their concerns over the fact that a list of all the risks can put a barrier to the development of geothermal energy and send an alarming message. Here, it is important to state that this risk identification phase is necessary to assess and mitigate those risks in the subsequent phases. However, the remark from the stakeholders is understandable and the register will be improved by also indicating the various existing measures and good practice applied by the geothermal operators for de-risking the projects. Moreover, a distinction should be made between the types of mitigation measures: technical measures, financial measures (such as insurances) and political measures (such as new laws or subsidies).

Another concern from some stakeholders was the fact that they thought some risks were irrelevant even if they seemed plausible. The reason behind this is that, for operators, some risks are covered by contract with their subcontractors, or other are covered by insurances, for instance during the drilling operations. This remark showed the need to highlight also the risk ownership: some risks are supported by the operator directly, but others are transferred to other stakeholders such as subcontractors or insurances. However, it is important that most of the risks are still indicated in the register as the goal of the project is to identify all barriers to the development of geothermal energy.

Regarding the comprehensive character of the register, the choice was made to reduce the number of risks. This does not mean that some risks are omitted, only that they are described with fewer details. A risk register is a form of communication medium and it is thus important to keep it manageable. If the level of details is too low, it is not necessary to increase it for all risks: it is sufficient to run a risk analysis first, and to work on the details of only those risks where a decision is harder to take such as the type of mitigation action to undertake. Risk analysis is described in the following section.

RISK ANALYSIS

After the risk identification phase, where many plausible risks are listed and described, it is often necessary to perform a risk analysis where risk are ranked and prioritized. This is because there is only limited resources that can be devoted to the reduction of these risks so it is important that these resources are put where it is the most efficient.

Method

In this project, risk analysis is somewhat different from a "regular" risk analysis: it is not applied to a particular project with its own data but on the deep geothermal energy industry a whole. Moreover, the intent is to be able to make distinction from country to country so different risk analyses should be run for each target country in the project. The objectives of the project, and the resources available for performing this task excluded the possibility of running a quantitative risk analysis based on data and modelling. Instead, it was decided to perform a qualitative risk analysis, and to rely on the expertise of national stakeholders in order to assess the risks. This was also justified by the fact that most data regarding risks are not published and are only available through the experience of the practitioners of the geothermal energy sector. A questionnaire was thus built and sent to a list of stakeholders from the GEORISK countries. In some cases, workshop were organized in order to assist in answering the questionnaire and to promote the activities of the project.

According to the ISO 31000 norm on risk management, "risk is often expressed in terms of a combination of the consequences of an event and the associated likelihood of occurrence". Likelihood is often expressed numerically in the form of a probability or a frequency, often depending on the type of risk and on the common practice of the industry. Consequences can be expressed by many indicators, but it is often expressed in losses or equivalent losses which are commonly evaluated in monetary terms (euros or dollars).

In order to keep the analysis simple, both likelihood and consequences are expressed with a score from a 4-point scale: 1 corresponding to least likely and least severe, and 4 corresponding to most likely and most severe. See Figure 1.

Figure 1: table explaining the scoring system for risk analysis

Regarding consequences (or "damage value" in the table), loss thresholds were indicated, but the respondents were asked to adjust these values in order to better correspond to the national context. Not all consequences are of economic nature, so for scoring, the table should be interpreted as "equivalent losses". For instance, regarding an environmental loss (such as pollution), it can be converted to equivalent monetary loss by answering one of the following question:

-"price you are willing to pay in order to avoid this consequence" (corresponding to the cost of preventive mitigation actions) -"price you will need to pay in order to remediate the consequences" (corresponding to the cost of remediation or repair).

The principle of the questionnaire is that for each risk in the register, the respondent should indicate two scores between 1 and 4: one corresponding to the perceived likelihood of the risk, and one corresponding to the perceived losses of the occurrence of the risk.

Another field in the questionnaire asked to give a score (again in a 4-point scale) in relation to the relevancy of the risk. This was judged necessary in order to distinguish risks that are known and easily mitigated (hence "not relevant" with respect to the goal of this risk analysis) with risks that creates problems irrespective of the actual level of the risk (for instance the "induced seismicity" risk is certainly highly relevant but is potentially low in many geological context). This was also a way to take into account the remark from the stakeholders' workshops regarding risk ownership (see the discussion of the risk register section). Figure 2 provides the details regarding this field.

Risk/Likelihood Level Damage Value Likelyhood 1 X < 10.000 € Not likely to occur 2 10.000 € < X < 100.000 € Mild chances of occurrence 3 100.000 € < X < 1.000.000 € Moderate chances of occurrence 4 X > 1.000.000 € High Chances of occurrence For sedimentary reservoirs, it was also possible to indicate a range of depth.

The idea behind this information is that each questionnaire should correspond to one type of project according to the subdivision provided. It was judged as a good compromise between project level and the generic level.

The last distinction necessary corresponds to the respondents themselves. Not all stakeholders have the same level of knowledge or the same expertise, and the register covers a very wide range of topics from socio-economics to technical issues. The solution was thus to ask to each respondent to auto-assess its own level of expertise regarding the following areas of expertise:

-Geology; -Social / Economic; -Drilling; -Operation / Development.

Another 4-point scoring scale was used for this, corresponding from "scarce understanding of the field" to "professional understanding of the field". Each risk in the register was then classified according to these levels of expertise, and the respondents could choose to answer only part of the questionnaire.

When the questionnaires are returned, they are grouped by countries/geology/type of energy representing one type of project. Inside each group, an average risk score is computed for each risk in the register. The risk score is computed using the following formula:

RS = 𝐿 + D (1)
Where RS is the risk score, L is the averaged likelihood and D is the averaged damage level. The averaged likelihood is computed from several respondents in the following way:

𝐿 = ∑ 𝑤 𝑖 𝐿 𝑖 𝑖 ∑ 𝑤 𝑖 𝑖 (2)
Where i corresponds to the i th respondent, Li is his estimation of the likelihood (a number between 1 and 4), and wi is the weight corresponding to his level of expertise:

-The highest level of expertise has a weight of 1; -

The second highest a weight of 0.875; -

The third highest a weight of 0.75; -

The lowest level of expertise a weight of 0.5.

This weight varies depending also on the risk because the level of expertise vary from an area to another (e.g. a drilling expert may not be an expert in the socio-economic area). Using this formula, the average is slightly modified by giving more weight to the persons with the highest level of expertise and less weight in the average to the persons with the lowest level of expertise.

The averaged damage level D is computed in a similar way, using the same weights.

Regarding the relevance score, for the average, all the weights are set to 1, meaning that all respondents have the same weights irrespective of their level of expertise. In equation ( 1), the operator used is a sum, but a product is sometimes used for computing the risk score. The reason is that the score for damage level is proportional to a power of ten (see figure 1). It is also similar for likelihood (e.g. the score corresponds roughly to probabilities of 10%, 1%, 0.1% and 0.01%). The actual product of likelihood and damage level, corresponding to the computation of the expected loss, is thus proportional to the sum of the score (see equation ( 3)).

10 𝑎 × 10 𝑏 = 10 𝑎+𝑏 (3)

Discussion

The results show that the initial objective of the project is achieved and that a ranked list of risk can be obtained for many contexts in Europe. Moreover, the contact with stakeholders from all the studied countries has been excellent with many useful and constructive feedbacks. It is not yet possible to give a precise number but the response rate to the questionnaire has been impressive considering it is quite lengthy and not trivial.

However, the qualitative method presented in this paper introduces some biases or errors. The main source of errors is the fact that the analysis relies almost exclusively on experts' judgment for computing the scores. A weighted average has been introduced in order to take account of different level of expertise but this cannot correct all the biases, including the possible misunderstanding of some risk descriptions, the biases due to the ordering of the risks which was the same for all respondents, and the possible confusion between the risk score and the relevance score. This last indicator was proposed in order to overcome some shortcomings, but the exact use of the values is not clear at the moment and from a preliminary analysis of the questionnaires, there seems to be a high discrepancy in the way the respondents interpreted this field.

Moreover, the scoring system can introduce additional errors: only considering the likelihood, the most robust mathematical representation is with probabilities (i.e. a real number between 0 and 1), that our system "downgrades" to a natural number between 1 and 4. Necessarily, some nuances are lost in this process, and the same observation is valid for damage values as well.

This means that the results presented here should be interpreted with caution. Each risk is associated with numbers but those numbers should not be seen as proportional to the actual level of risk, rather they are representative of the opinion of stakeholders (at least the one who responded) regarding the risks. In this regard, it would be interesting to assess -at least qualitatively -the representativeness of the respondents with respect to the whole cartography of professional stakeholders in the geothermal energy sector.

PERSPECTIVES

The next step is to analyze the data from the questionnaires from the different countries and geological settings, and to extract the commonalities as well as the main differences between the groups. One remark received from a respondent was that the study should not lead to the conclusion that one country or one type of geothermal energy has "more risks" than other countries or types of geothermal energy. This could be avoided for instance by normalizing the results (i.e. all values for the risk score are converted to a number between 0 and 1, with 0 corresponding to the lowest risk and 1 to the highest risk).

After the finalization of the risk analysis step, the stated objective of the GEORISK project is to provide an online tool. This tool will present the main results as showed in this paper: the risk register and the result of the risk analysis. In addition, an important focus will be put on possible measures for de-risking the projects. The possibility of financial mitigation will then be improved by further work from the GEORISK project.

CONCLUSION

Despite the quantity of previous work existing on the topics of risks and geothermal energy, the work presented here is to our knowledge one of the most comprehensive as it tackles many kinds of risks (including socio-economic risks, geological risks, environmental risks), and many contexts that are mainly relevant to Europe but non only. In a first step, an important synthesis of available knowledge has been performed in order to build a generic risk register. The following step consisted in giving a score to these risks, in the risk analysis step. The analysis considers different kind of energy use, different geological settings and different countries. The scoring relies on experts' judgment and a questionnaire was built and sent to stakeholders from the studied countries.

The results from one group showed that that risks due to uncertainties in the subsurface, also known as geological risks, are perceived as the most impacting, and should thus be the focus of the development of innovative de-risking measures, including financial measures, such as insurance schemes, which is the main purpose of the GEORISK project.

Figure 2 :

 2 Figure 2:Table provided to respondents for assessing the risk relevance

  current version of the risk register contains 54 individual risks subdivided in six categories. The description of all the risks is available in the report Le Guenan et al. (2019). The categories were created afterwards and were not used in a top-down manner for finding new risks. These categories are:

	-	External hazards (2 risks);
	-	Risks due to uncertainties in the external context (9 risks);
	-	Risks due to internal deficiencies (9 risks);
	-	Risks due to subsurface uncertainties (17 risks);
	-	Technical issues (9 risks);
	-	Environmental risks (8 risks).

Table provided to respondents for assessing the risk relevance

  In addition, respondents were asked to provide further information: type of output, reservoir type, country / region, and level of expertise. Type of output is either heat or electricity (including Combined Heat and Power, CHP). For reservoir types, it was also decided to keep things simple, and the following choices were proposed:

	-	Basement rocks;
	-	Volcanic zones:
	-	Sedimentary.

ACKNOWLEDGEMENTS

The authors wish to thank the partners of the H2020 project GEORISK who helped in building the register and the questionnaire for risk assessment. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 818232.

Another reason is that using a product with the likelihood and the damage scores would change the ordering: a risk with scores [3,3] would score higher than a risk with scores [2,4] with the product whereas they would be ranked similarly with the sum (Figure 3).

Results

At the time of writing the paper, not all the results were collected. We present here only the results for one of the groups for which 7 questionnaires were returned. All the respondents filled all the required fields. The results correspond to the case of heat generation, in the country of Hungary, in sedimentary reservoirs with a depth inferior to 2 km.

By using the formula of equation ( 2), there were fifteen risks which obtained a score of 5 or more:

-"Low financing for work leading to low safety standards" (5.2) -"Demand analysis and forecast are inaccurate" (5.1) -"Flow rate lower than expected (reservoir)" (5.1) -"Pressure lower/higher than expected" (5.2) -"Neighboring operators cause negative changes to the reservoir parameters" (5.1) -"Fluid chemistry / gas content / physical properties are different from expected" (5.1) -"Target formation is missing in the well (unexpected geology, insufficient exploration)" (5.7) -"Target formation has no/insufficient fluid for commercial production" (5.8) -"Geological lithology or stratigraphy is different than expected" (5.1) -"Particle production ("sanding")" (5.3) -"Hydraulic connectivity between wells is insufficient for commercial use" (5.0) -"Re-injection of the fluid is more difficult than expected" (5.2) -"Degradation of the reservoir (structure, properties, deteriorating whole-scale further commercial utilization)" (5.9) -"Loss of integrity of surface equipment (leakage from the tanks, pipeline, heat-exchanger, etc.) (5.4) -"Damage to the well/reservoir while drilling or testing" (5.4)

Considering the categories introduced in the risk register, most (10) of these "top risks" are from the "risks due to subsurface uncertainties" category.

A relevance score was also computed for all the risks. 12 risks have a score of 3 or higher (out of 4):

-"Lack of financing for the next phases" (3.3) -"Significant changes of energy costs" (3.3) -"Unanticipated delays and costs in operations (materials, services, maintenance)" (3.1) -"Unsuitable contracts (roles and responsibility not clearly defined) leading to suboptimal performance or exploding costs" (3.6) -"Fluid chemistry/ gas content / physical properties are different from expected" (3.4) -"Target formation is missing in the well (unexpected geology, insufficient exploration)" (3.4) -"Target formation has no/insufficient fluid for commercial production" (3.6) -"Geological lithology or stratigraphy is different than expected" (3.6) -"Particle production ("sanding")" (3.1) -"Degradation of the reservoir (structure, properties, deteriorating whole-scale further commercial utilization)" (3.1) -"Damage to the well/reservoir while drilling or testing" (3.1) -"Well casing collapse" (3.1)

Only 7 risks are present in both lists. In fact, the correlation coefficient between both indicators (risk score and relevance score) is 0.64, showing that despite the tendency for respondents to give a high relevance to the highest risks, the difference between the indicators is not negligible and so both bring a different point of view on the risks. It is not clear however how the relevance score can be exploited in the final ranking.

1 2 3 4 1 2 3 4 1 2 3 4 5 1 1 2 3 4 2 3 4 5 6 2 2 4 6 8 3 4 5 6 7 3 3 6 9 12 4 5 6 7 8 4 4 8 12 16


Claudine Ménard

Pierre Charnet

Matthieu Rousset

Michel Vignes

Thierry Cens
email: thierry.cens@inserm.fr.

Cav2.1 C-terminal fragments produced in Xenopus laevis oocytes do not modify the channel expression and functional properties

Keywords: Ca 2+ channel, Xenopus laevis oocytes, electrophysiology, co-immunoprecipitation assay

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

lacks the GGCAG insertion, but harbors a sequence closely related to that found in human exon 47 [START_REF] Andrés-Mateos | Bovine CACNA1A gene and comparative analysis of the CAG repeats associated to human spinocerebellar ataxia type-6[END_REF]. Conversely, the rabbit BI-2 variant results from the removal of the exon 46 end and the first third of exon 47 [START_REF] Mori | Primary structure and functional expression from complementary DNA of a brain calcium channel[END_REF]. Similar recombination events have also been described in the human CACNA1A gene, although they lead to isoforms that are completely different from BI-2 [START_REF] Zhuchenko | Autosomal dominant cerebellar ataxia (SCA6) associated with small polyglutamine expansions in the α1Avoltage-dependent calcium channel[END_REF][START_REF] Veneziano | Newly characterised 5' and 3' regions of CACNA1A gene harbour mutations associated with Familial Hemiplegic Migraine and Episodic Ataxia[END_REF]. A mouse isoform with a fully translated exon 47, similar to the human sequence, has been described [START_REF] Richards | Novel CaV2.1 clone replicates many properties of Purkinje cell CaV2.1 current[END_REF], but other isoforms also exist. For instance, MPII (BAB85612) results from the use of an alternative acceptor site at the beginning of exon 47 (Fig. 1) that leads to the expression of a unique sequence up to the deletion of a single base allowing the recovery of the original reading frame of the fully translated exon 47. Therefore, MPII C-terminus is close to that of the human subunit [START_REF] Tsunemi | Novel Cav2.1 splice variants isolated from Purkinje cells do not generate P-type Ca2+ current[END_REF]. In the MPI isoform (BAB85611), deletion of 150 nucleotides in exon 47 produces a translational frameshift, leading to a specific C-terminal sequence [START_REF] Tsunemi | Novel Cav2.1 splice variants isolated from Purkinje cells do not generate P-type Ca2+ current[END_REF]. Given the high degree of sequence conservation in Cav2.1 and the similarities in the CACNA1 gene organization in different species [START_REF] Soong | Systematic identification of splice variants in human P/Q-type channel alpha1(2.1) subunits: implications for current density and Ca2+-dependent inactivation[END_REF][START_REF] Kanumilli | Alternative splicing generates a smaller assortment of CaV2.1 transcripts in cerebellar Purkinje cells than in the cerebellum[END_REF], such variability in the Cav2.1 C-terminus is quite intriguing. Moreover, it is not clear whether and how these specific sequences affect the functional properties of Cav2.1 channels.

Interestingly, when the human Cav2.1 subunit is expressed in HEK cells, a 60-75kDa fragment (1ACT) that contains a tract of glutamine residues (polyQ) can be detected in cell nuclei with antibodies against the Cav1 C-terminus [START_REF] Kubodera | Proteolytic cleavage and cellular toxicity of the human alpha1A calcium channel in spinocerebellar ataxia type 6[END_REF][START_REF] Kordasiewicz | C-termini of P/Qtype Ca2+ channel alpha1A subunits translocate to nuclei and promote polyglutaminemediated toxicity[END_REF].

The 1ACT protein is translated through an internal ribosome entry site (IRES) located within the mature mRNA encoding the Cav2.1 subunit [START_REF] Du | Second cistron in CACNA1A gene encodes a transcription factor mediating cerebellar development and SCA6[END_REF], and may act as a transcription factor that modulates the expression of genes involved in neural development [START_REF] Du | Second cistron in CACNA1A gene encodes a transcription factor mediating cerebellar development and SCA6[END_REF].

Importantly, 1ACT harboring a pathologic polyQ expansion, as observed in spinocerebellar ataxia type 6 (SCA6), cannot upregulate these genes [START_REF] Du | Second cistron in CACNA1A gene encodes a transcription factor mediating cerebellar development and SCA6[END_REF][START_REF] Ishida | Vulnerability of Purkinje Cells Generated from Spinocerebellar Ataxia Type 6 Patient-Derived iPSCs[END_REF][START_REF] Bavassano | Bicistronic CACNA1A Gene Expression in Neurons Derived from Spinocerebellar Ataxia Type 6 Patient-Induced Pluripotent Stem Cells[END_REF]. SCA6 is an autosomal dominant cerebellar ataxia caused by expansion of this polyQ tract and characterized by the formation of insoluble aggregates in cerebellar Purkinje cells. Due to alternative splicing, the polyQ expansion is present in the Cav2.1 subunit with an extended C-terminus, but not in the short isoforms [START_REF] Zhuchenko | Autosomal dominant cerebellar ataxia (SCA6) associated with small polyglutamine expansions in the α1Avoltage-dependent calcium channel[END_REF]. Studies on the functional impact of the pathological polyQ expansion in Cav2.1 gave rise to discrepancies, probably due to differences in the used Cav2.1 isoforms and/or cell lines [START_REF] Matsuyama | Direct alteration of the P/Q-type Ca2+ channel property by polyglutamine expansion in spinocerebellar ataxia 6[END_REF][START_REF] Restituito | The polyglutamine expansion in spinocerebellar ataxia type 6 causes a beta subunit-specific enhanced activation of P/Q-type calcium channels in Xenopus oocytes[END_REF][START_REF] Toru | Spinocerebellar ataxia type 6 mutation alters P-type calcium channel function[END_REF][START_REF] Piedras-Renteria | Increased expression of alpha 1A Ca2+ channel currents arising from expanded trinucleotide repeats in spinocerebellar ataxia type 6[END_REF].

However, the functional properties of human Cav2.1 harboring a pathological polyQ expansion are not affected in Purkinje cells isolated from a knock-in mouse model of SCA6 [START_REF] Saegusa | Properties of human Cav2.1 channel with a spinocerebellar ataxia type 6 mutation expressed in Purkinje cells[END_REF].

Unlike many other species, the rat and mouse Cacna1a genes are devoid of CAG repeats [START_REF] Andrés-Mateos | Bovine CACNA1A gene and comparative analysis of the CAG repeats associated to human spinocerebellar ataxia type-6[END_REF], thus precluding the additional level of complexity of 1ACT functions brought by the polyQ expansion. Rat Cav2.1 with an extended C-terminus encoded by exon 47 (called herein Cav2.1(e47)) (Fig. 1) has not been functionally characterized yet and only the short variant Cav2.1 (called herein Cav2.1) is currently available for functional studies.

Therefore, here we used Xenopus laevis oocytes, a heterologous expression system that has been used to study Cav2.1 calcium channels for nearly three decades, to determine whether a similar C-terminal fragment is produced also in oocytes that express rat Cav2.1, and whether co-expression of this fragment and Cav2.1 affects the channel properties. Indeed, Cav1 Cterminus interacts with the Cav4 subunits or other regions of the Cav subunit [START_REF] Sandoz | Distinct properties and differential beta subunit regulation of two C-terminal isoforms of the P/Qtype Ca(2+)-channel alpha(1A) subunit[END_REF][START_REF] Geib | The interaction between the I-II loop and the III-IV loop of Cav2.1 contributes to voltage-dependent inactivation in a betadependent manner[END_REF]. To this aim, we cloned a novel variant of the extended rat Cav2.1 Cterminus (Cav2.1(e47s)), in addition to Cav2.1(e47). We found that the electrophysiological properties of Cav2.1(e47) and Cav2.1(e47s) do not differ from those of Cav2.1, when expressed in X. laevis oocytes with Cav4a, 1b or 2a. Moreover, Cav2.1, Cav2.1(e47) and Cav2.1(e47s) produced C-terminal fragments equivalent to those produced by the human gene. These short and long C-terminal fragments (CT1 and CT1(e47), respectively) interacted with Cav4a in coimmunoprecipitation assays. Co-expression of C-terminal fragments with Cav2.1 or with a truncated Cav2.1 that lacks most of the C-terminus did not affect the channel expression or functional properties. Our study brings new important findings on rat Cav2.1 isoforms and clarifies the role of the C-terminal fragments expressed with the full-length channel in X. laevis oocytes.

MATERIAL AND METHODS

Molecular biology

The rat brain Cav2.1 (Genbank accession number AAA40806), Cav1b (CAA43665), Cav2a (AAK14821), Cav4a (A45982), and Cav21 (AAG28164) cDNAs were cloned in the pMT2 and pcDNA3.1(+) (for Cav21) expression vectors. The fragment encoding Cav2.1 exon 47 was obtained by RT-PCR using specific oligonucleotides and rat cerebellum total RNA (Agilent Technologies). Sequencing of several clones after cloning in the pBS-SK vector (Agilent technologies) revealed the existence of two fragments: TCA50 (full length exon 47) and TCA56 (with a 213nt deletion). They were cloned in frame with rat Cav2.1 in pMT2 using conventional molecular biology techniques to obtain Cav2.1(e47) and Cav2.1(e47s), respectively. Cav2.1, Cav2.1(e47) and Cav2.1(e47s) were also cloned in the pEGFP-N1 expression vector (Clontech) in frame with GFP to obtain Cav2.1-GFP, Cav2.1(e47)-GFP and Cav2.1(e47s)-GFP. The Cterminal fragments CT1, CT1(e47), CT2, and CT2(e47) were PCR amplified, cloned in frame with the RGS-6His tag in a modified pEGFP-N1 vector in which the GFP-encoding sequence was exchanged with the sequence encoding the RGS-6His tag to obtain CT1-6His, CT1(e47)-6His, CT2-6His, and CT2(e47)-6His. CT1 and CT1(e47) were also cloned in pEGFP-N1 in frame with GFP to generate CT1-GFP and CT1(e47)-GFP. For the production of Cav2.1CT1 and Cav2.1CT2, a stop codon was introduced by PCR after residue K1906 (numbered according to the AAA40806 sequence) or A2043, respectively. The amplified C-terminal fragments were cloned in pBS-SK, fully sequenced, and then, conventional molecular biology techniques were used to put them at the place of the full length Cav2.1 C terminus. For X. laevis oocyte injections, a cDNA mixture at a molar ratio of 1:1:1 (Cav:Cav:Cav2) was prepared with the addition of water or the cDNA encoding one of the C-terminal fragments tagged with RGS-6His at a molar ratio of 1:1:1:1, unless otherwise indicated.

Ethical statement

This study was carried out in strict accordance with the recommendations and relevant guidelines of our institution. Surgery was performed under anesthesia, and efforts were made to minimize suffering. The care and use of Xenopus conformed to institutional policies and guidelines. The experimental protocols were approved by the "Direction Départementale des Services Vétérinaires" (authorization N° C34.16).

X. laevis oocyte preparation and injection

Preparation and injection of X. laevis oocytes were done as previously described [START_REF] Cens | Modulation of the α1A Ca2+ channel by β subunits at physiological Ca2+ concentration[END_REF]. After injection of about 15 ng of cDNA, oocytes were maintained at 19°C in NDS buffer (96 mM NaCl, 2 mM KCl, 1.8 mM CaCl2, 1 mM MgCl2, 5 mM Hepes, 2.5 mM Na-Pyruvate, 0.05 mM gentamycin, pH 7.2 with NaOH) renewed daily.

Electrophysiology

Expressed currents were recorded at room temperature using the two-electrode voltage clamp method at day 1-3 after injection, as previously described [START_REF] Cens | Introduction into Ca(v)2.1 of the homologous mutation of Ca(v)1.2 causing the Timothy syndrome questions the role of V421 in the phenotypic definition of P-type Ca(2+) channel[END_REF]. Electrodes were pulled from borosilicate glass and filled with 3M KCl. Currents were recorded with a Geneclamp 500 amplifier (Molecular Devices), and digitized with a Digidata 1200 converter (Molecular Devices) using the Clampex software (Molecular Devices). The external solution (10mM BaOH, 20mM TEAOH, 2mM CsOH, 50mM N-methyl-D-glucamine, 10mM HEPES, pH 7.2 with methane sulfonic acid) was continuously perfused in the recording chamber at the rate of 1 mL/min.

Currents were elicited by a two-pulse protocol from a holding potential of -100 mV. The first pulse lasted for 2.5 s and varied from -80 to +50 mV in 10 mV increments. The second pulse, separated from the first pulse by a 10 ms interval at -100 mV, lasted for 400 ms and was set at +10 mV. The isochronal inactivation curves were obtained by measuring the peak amplitude of the current evoked by the second pulse and by plotting the normalized current values (I/Imax) as a function of the voltage reached during the first pulse. They were fitted using the following equation:

I/Imax = Rin + (1-Rin)/(1+exp((V-Vi)/k))
where I is the current amplitude measured during the second pulse following a first depolarization to V, Imax is the current amplitude measured during the second pulse following a first pulse at -80 mV, Vi is the half inactivation potential, k is a slope factor and Rin is the portion of non-inactivating current.

Currents were also elicited by single 400 ms-long depolarizations varying from -60 mV to +40

mV, from a holding potential of -100 mV. Current-voltage curves were obtained by measuring the peak amplitude and by plotting the normalized current values (I/Imax) as a function of the voltage. They were fitted using the following equation:

I/Imax = G (V-Erev)/(1+exp((V-Va)/k))
where I is the current amplitude measured during the depolarization to V, Imax is the peak current amplitude measured at the maximum of the current-voltage curve, G is the normalized macroscopic conductance, Erev is the apparent reversal potential, Va is the potential for half activation, and k is a slope factor. The inactivation velocity (R400) was estimated as the percentage of the peak current that disappeared after 400ms-long depolarizations to +10 mV.

Recovery from inactivation was determined using a protocol with two pulses at +10 mV for 5 s or 100 ms, respectively, from a holding potential of -100 mV, separated by an interval of time between 100 ms and 8 s. Traces were normalized to the maximum current measured during the first pulse. The percentage of current recovered during the second pulse was plotted as a function of the inter-pulse interval. Traces were fit with a double exponential equation.

Western blotting and immunoprecipitation assays

Oocytes that expressed Cav1-GFP alone or with 6His-tagged C-terminal fragments were harvested in 10 mM Tris HCl pH 7.5, 1 mM PMSF, 1 mM EDTA pH 8, and Complete Protease Inhibitor (Roche). Lysates were homogenized with one volume of 2% Triton X100, 20 mM Tris pH 7.5, 300 mM NaCl, 1 mM EDTA, and incubated on ice for 1 hour. A volume of lysate corresponding to ~2 oocytes (i.e., an amount sufficient to generate ~4A total current amplitude measured at +10 mV) was separated on Mini-PROTEAN® TGX TM Pre-Cast Gels (BioRad).

Immunoblotting was performed with rabbit anti-GFP (1:5000, Sigma-Aldrich Cat#SAB4301138, RRID: AB_2750576), and mouse anti-RGS6His (1:1000, QIAGEN Cat#3460, RRID: AB_2687898), and horseradish peroxidase-conjugated goat anti-rabbit (1:5000, Sigma-Aldrich Cat#A0545, RRID: AB_257896) antibodies or horseradish peroxidaseconjugated goat anti-mouse (1:5000, Sigma-Aldrich Cat#A9917, RRID: AB_258476).

HEK293 cells (RRID: CVCL_0045) were transfected with CT1-GFP or CT1(e47)-GFP alone, or together with Cav4 using Lipofectamine (ThermoFisher). At 24h post-transfection, cells were harvested in RIPA Lysis Buffer (Boster) with 1 mM PMSF, 1 mM EDTA pH 8, and Complete Protease Inhibitor. Cell lysates were incubated with a prewashed slurry of 20 l anti-GFP-Trap® A beads (Chromotek) at 4°C for 2h. Immunoprecipitates were recovered in 0.2 M glycine pH 2.5. Cells lysates and immunoprecipitates were analyzed separately using rabbit anti-GFP (1:5000), mouse anti-Cav4 (1:1000, abcam Cat#85788, RRID: AB_1860050), rabbit anti-GAPDH (1:5000, Sigma-Aldrich Cat#G9545, RRID: AB_796208), and horseradish peroxidase-conjugated goat anti-rabbit (1:5000) or goat anti-mouse (1:5000) antibodies.

Detection was performed with the Western Lightning Plus (Perkin Elmer) reagents.

Data analysis

Results were expressed as the mean ± SEM. The statistical significance of differences between two groups was determined using the non-paired Student's t test.

RESULTS

Cloning and functional expression of two long variants of the rat Cav2.1 subunit

A cDNA fragment (AAC24516) encoding the entire exon 47 of the rat Cav2.1 calcium channel was previously isolated from pancreas [START_REF] Ligon | Class A calcium channel variants in pancreatic islets and their role in insulin secretion[END_REF], and a partial clone that lacks 123 nucleotides in exon 47 sequence (CAJ09863) was obtained from rat Purkinje neurons [START_REF] Kanumilli | Alternative splicing generates a smaller assortment of CaV2.1 transcripts in cerebellar Purkinje cells than in the cerebellum[END_REF](Fig. 1). However, to our knowledge, no cDNA encoding the rat Cav2.1 with the long C-terminus is available for functional studies. Therefore, we designed oligonucleotides to amplify rat exon 47 from cerebellum RNA and obtained two different variants. The first one (TCA50, Fig. 1) encoded the entire exon 47 and was identical to the AAC24516 sequence and similar to a clone (AAW56205) isolated from mouse brain [START_REF] Richards | Novel CaV2.1 clone replicates many properties of Purkinje cell CaV2.1 current[END_REF]. The second variant (TCA56, Fig. 1) lacked 213 nucleotides compared with TCA50. The deletion in TCA56 was different from the one found in CAJ09863 (Fig. 1) and produced a novel rat Cav2.1 variant. Both deletions were similar to those found in mouse Purkinje neurons [START_REF] Tsunemi | Novel Cav2.1 splice variants isolated from Purkinje cells do not generate P-type Ca2+ current[END_REF], but due to frameshifts of the open reading frame, the amino acid sequences of the mouse variants differ substantially at the N-(BAB85612) or the C-terminus (BAB85611) of exon 47 (Fig. 1b). We constructed two cDNAs [Cav2.1(e47) and Cav2.1(e47s)] that encode the rat Cav2.1 subunit with full length exon 47 (i.e., TCA50) and the shorter variant (i.e., TCA56), respectively (Fig. 2a). When co-expressed with the Cav4a and Cav21 subunits in X. laevis oocytes (Fig. 2b), Cav2.1, Cav2.1(e47) and Cav2.1(e47s) displayed globally similar electrophysiological properties (Fig. 2c and Table 1). The inactivation kinetics, estimated using the mean R400 values, were also comparable (Fig. 2c and Table 1). We obtained similar results when the three Cav2.1 variants were co-expressed with Cav1b or Cav2a that accelerates or decelerates inactivation, respectively, instead of Cav4a (Table 1). We next investigated the recovery from inactivation that is slower for the human long Cav2.1 isoform compared with the short isoform [START_REF] Adams | Ca(V)2.1 P/Q-type calcium channel alternative splicing affects the functional impact of familial hemiplegic migraine mutations: implications for calcium channelopathies[END_REF]), but we did not observe this difference when using rat Cav2.1 and Cav2.1(e47) or Cav2.1(e47s) (Fig. 3).

3.2

The rat Cav2.1 transcripts produce C-terminal fragments in X. laevis oocytes Human long Cav2.1 produces a C-terminal fragment (1ACT) when expressed in HEK or PC12 cells [START_REF] Kubodera | Proteolytic cleavage and cellular toxicity of the human alpha1A calcium channel in spinocerebellar ataxia type 6[END_REF][START_REF] Kordasiewicz | C-termini of P/Qtype Ca2+ channel alpha1A subunits translocate to nuclei and promote polyglutaminemediated toxicity[END_REF][START_REF] Ishiguro | The carboxyterminal fragment of alpha(1A) calcium channel preferentially aggregates in the cytoplasm of human spinocerebellar ataxia type 6 Purkinje cells[END_REF]. However, little is known about the Cav2.1 isoform with the shorter C-terminus and whether C-terminal fragments are produced when the channel is expressed in X. laevis oocytes. Therefore, we coinjected the Cav4 and Cav21 subunits with rat Cav2.1-GFP, Cav2.1(e47)-GFP, or Cav2.1(e47s)-GFP in X. laevis oocytes and selected expressing oocytes. Western blot analysis with an anti-GFP antibody (Fig. 4a) showed the presence of full-length Cav1 (band above 250kDa) and of additional bands between 60-90kDa. This demonstrated that rat Cav2.1 produces C-terminal fragments, like human Cav2.1, independently of the presence of exon 47.

These C-terminal fragments might influence the expression and electrophysiological properties of Cav2.1 channels. Indeed, it was previously shown that Cav4 interacts specifically with a Cterminus binding domain (SS2) in the rabbit Cav2.1 isoforms BI-1 and BI-2 [START_REF] Walker | A new beta subtype-specific interaction in alpha1A subunit controls P/Q-type Ca2+ channel activation[END_REF][START_REF] Sandoz | Distinct properties and differential beta subunit regulation of two C-terminal isoforms of the P/Qtype Ca(2+)-channel alpha(1A) subunit[END_REF]. Therefore, we generated two Cav2.1 C-terminal fragments that start at residue I1907 (according to the AAA40806 sequence) and encompass the full-length exon 47 (CT1(e47)) or the C-terminal sequence of the Cav2.1 isoform without exon 47 (CT1) (Fig. 5a). We then expressed C-terminally GFP-tagged CT1 and CT1(e47) together with Cav4 in HEK293 cells. Co-immunoprecipitation assays of protein lysates showed that both CT1 and CT1(e47) interacted with Cav4 (Fig. 4b). These findings indicate that in X. laevis oocytes functional Cav2.1 channels coexist with C-terminal fragments, and that these fragments can interact with channel partners, such as Cav4a.

Over-expression of C-terminal fragments do not affect Cav2.1 expression and functional properties in X. laevis oocytes

To evaluate the functional role of the Cav2.1 C-terminus and the potential influence of the different C-terminal fragments, we also generated a rat Cav2.1 subunit that lacks the CT1 fragment (Cav2.1CT1, Fig. 5a). When co-expressed with Cav4 and Cav21 in X. laevis oocytes, Cav2.1CT1 did not produce functional channels (Fig. 5b) even after co-expression of CT1 (not shown). Moreover, co-injection of Cav4 and Cav21 with Cav2.1 and CT1 or CT1(e47) (1:1:1:1 molar ratio) did not, or marginally, affect the parameters of the currentvoltage curves, of the inactivation curves, and the R400 values (Fig. 5c and Table 1). We confirmed the expression of the His-tagged C-terminal fragments in X. laevis oocytes by Western blot analysis after the recordings (Fig. 5d). As these fragments interact with the Cav4 subunit (see Fig. 4b), we tested whether they could inhibit Cav2.1 expression when injected in excess relative to the cDNA encoding Cav2.1, Cav4 and Cav21 (molar ratio 1:1:1:5).

However, the percentage of expressing oocytes was similar when Cav2.1 was expressed alone or with CT1 or CT1(e47) (44%, 43%, and 36%, respectively, four independent experiments) (Fig. 5e (left panel)), suggesting that the C-terminal fragments did not inhibit Cav2.1 expression by titrating the Cav4 subunit. We confirmed this lack of effect by systematically measuring the current amplitude in all the expressing oocytes in a single experiment. Indeed, we found similar mean current amplitude values for Cav2.1 expressed alone or with CT1 or CT1(e47) (Fig. 5e (right panel)). Cav2.1 voltage-dependent properties and inactivation kinetics also were not affected after injection in excess of C-terminal fragments (Table 1).

The effect of the co-expressed C-terminal fragments could have been hindered by the presence of the C-terminus in the Cav2.1 subunit. As CT1 deletion (Cav2.1CT1) affects functional expression, we introduced a stop codon after residue A2043 (according to the AAA40806 sequence) just before the SS2 binding domain (Cav2.1CT2, Fig. 6a). When co-expressed with Cav4a and Cav21 in X. laevis oocytes, Cav2.1CT2 gave rise to functional channels (Fig. 6c). Moreover, Cav2.1CT2 electrophysiological properties were undistinguishable from those of Cav2.1 (Fig. 6d and Table 1). Co-expression with CT2 or CT2(e47) (Fig. 6a) did not modify Cav2.1CT2 properties (Fig. 6d and Table 1). We confirmed CT2 and CT2(e47) expression in the recorded oocytes by Western blotting (Fig. 6b). These results suggest that the lack of effects of the C-terminal fragments observed with full-length Cav2.1 was not due to the presence of the C-terminus in Cav2.1.

In this study, we cloned Cav2.1(e47) and Cav2.1(e47s), two C-terminal variants of the rat Cav2.1 subunit that harbor the full length (TCA50) or a shorter variant (TCA56) of exon 47 (Fig. 1) respectively, and analyzed their functional properties after expression in X. laevis oocytes. Whatever the co-expressed Cav subunit (Cav1b, Cav2a or Cav4a), the electrophysiological properties of Cav2.1(e47) and Cav2.1(e47s) were comparable to those of the short Cav2.1 isoform. Moreover, these three Cav2.1 isoforms produced C-terminal fragments, like the human CACNA1A gene. These fragments interacted with the Cav4a subunit in co-immunoprecipitation assays. However, overexpression of these C-terminal fragments with full length Cav2.1 did not modify the expression or the functional properties of the channel.

Exon 47 is present only in some Cav2.1 isoforms because the retention of the GGCAG sequence produces a frameshift of the reading frame (see Fig. 1). Based on our results, we can expect that exon 47 might be expressed in some isoforms of 1ACT. Comparison of the different Cterminus variants among species highlights differences in the events leading to their production and also in their sequences. On the other hand, when expressed in HEK293 cells, the three mouse Cav2.1 variants (the short MPc, and the two MPI and MPII variants) show similar electrophysiological properties [START_REF] Tsunemi | Novel Cav2.1 splice variants isolated from Purkinje cells do not generate P-type Ca2+ current[END_REF]. Although the C-terminal sequences of the mouse and rat variants are different, these results are consistent with our observations in X. laevis oocytes. The channel activation and inactivation processes are not affected by Cav2.1 variants with different C-terminal sequence (mouse MPI and MPII, and rat Cav2.1(e47) and Cav2.1(e47s)). Similar results were obtained in a study that compared the properties of human Cav2.1 with or without extended C-terminus (fully translated exon 47 with 11 Q residues in the polyQ expansion) co-expressed in HEK293 cells with Cav4 [START_REF] Adams | Ca(V)2.1 P/Q-type calcium channel alternative splicing affects the functional impact of familial hemiplegic migraine mutations: implications for calcium channelopathies[END_REF] or in another study that used human/mouse chimeric Cav2.1 channels also expressed in HEK293 cells (human Cav2.1 sequence with murine exon 47) [START_REF] Heck | Transient Confinement of CaV2.1 Ca2+-Channel Splice Variants Shapes Synaptic Short-Term Plasticity[END_REF]. However, the recovery from inactivation was slower and inactivation accumulation was greater for the long than for the short Cav2.1 variant [START_REF] Adams | Ca(V)2.1 P/Q-type calcium channel alternative splicing affects the functional impact of familial hemiplegic migraine mutations: implications for calcium channelopathies[END_REF]. Although the rabbit BI-1 and BI-2 variants are quite different (the C terminus of the long isoform BI-2 harbors an additional Cav binding site besides SS2) [START_REF] Sandoz | Distinct properties and differential beta subunit regulation of two C-terminal isoforms of the P/Qtype Ca(2+)-channel alpha(1A) subunit[END_REF], the functional properties of both subunits are very similar when expressed with Cav3 or Cav4. Indeed, the additional Cav binding domain in BI-2 seems to affect only the current density [START_REF] Sandoz | Distinct properties and differential beta subunit regulation of two C-terminal isoforms of the P/Qtype Ca(2+)-channel alpha(1A) subunit[END_REF]. The sequence of the Cav4 SS2 binding domain is well conserved among species, and our results show that rat Cav2.1 Cterminus binds to Cav4, as previously reported for the rabbit Cav2.1 subunit [START_REF] Walker | A beta 4 isoform-specific interaction site in the carboxyl-terminal region of the voltage-dependent Ca2+ channel alpha 1A subunit[END_REF][START_REF] Sandoz | Distinct properties and differential beta subunit regulation of two C-terminal isoforms of the P/Qtype Ca(2+)-channel alpha(1A) subunit[END_REF]. However, the results obtained with Cav2.1CT2 (without the SS2 binding domain) seem to exclude a functional role for this interaction site when the channel is expressed at the plasma membrane. A similar deletion in a mouse Cav2.1 variant did not affect expression or functional properties when expressed at the calyx of Held in a knock-in mouse model [START_REF] Lübbert | A novel region in the CaV2.1 α1 subunit C-terminus regulates fast synaptic vesicle fusion and vesicle docking at the mammalian presynaptic active zone[END_REF].

In the majority of the central nervous system synapses, P/Q-type calcium channels are the principal Ca 2+ channels that trigger the neurotransmitter release. This results from specific interactions between the Cav2.1 subunit and several scaffold proteins that mediate the link between Ca 2+ channels, synaptic vesicles, and the exocytosis machinery. Indeed, in Cav2.1, the cytoplasmic loop that links domains II and III contains a synprint interaction domain for the interaction with synaptic proteins, such as syntaxin-1 and synaptotagmin [START_REF] Rettig | Isoform-specific interaction of the alpha1A subunits of brain Ca2+ channels with the presynaptic proteins syntaxin and SNAP-25[END_REF][START_REF] Charvin | Direct interaction of the calcium sensor protein synaptotagmin I with a cytoplasmic domain of the alpha 1A subunit of the P/Q-type calcium channel[END_REF]. This domain is also found in the Cav2.2 subunit that encodes the N-type Ca 2+ channel, involved in neurotransmission. Interestingly, a Cav2.2 variant in which the synprint domain is missing has been identified in the human brain [START_REF] Kaneko | Identification and characterization of novel human Ca(v)2.2 (alpha 1B) calcium channel variants lacking the synaptic protein interaction site[END_REF]. Other sites for the interactions with RIM, MINT1, Rim Binding Proteins and CASK, important for the synaptic localization of the Cav2.1 subunit, are localized in Cav C-terminus [START_REF] Maximov | Association of neuronal calcium channels with modular adaptor proteins[END_REF][START_REF] Hibino | RIM binding proteins (RBPs) couple Rab3-interacting molecules (RIMs) to voltage-gated Ca(2+) channels[END_REF][START_REF] Kaeser | RIM proteins tether Ca2+ channels to presynaptic active zones via a direct PDZ-domain interaction[END_REF]. The relative importance of each of these interactions for Cav2.1 targeting to the presynaptic active zone is a matter of debate [START_REF] Lübbert | A novel region in the CaV2.1 α1 subunit C-terminus regulates fast synaptic vesicle fusion and vesicle docking at the mammalian presynaptic active zone[END_REF], but it is interesting that some of these binding sites are encoded by exon 47, and are therefore not found in all Cav2.1 variants [START_REF] Hirano | C-terminal splice variants of P/Q-type Ca2+ channel CaV2.1 α1 subunits are differentially regulated by Rab3interacting molecule proteins[END_REF]. In a recent study, Heck and colleagues found that the different release probability and short-term plasticity of hippocampal synapses that express Cav2.1 with or without exon 47 are due to a difference in surface mobility between these channels [START_REF] Heck | Transient Confinement of CaV2.1 Ca2+-Channel Splice Variants Shapes Synaptic Short-Term Plasticity[END_REF]. It is tempting to speculate that Cav2.1 isoforms with different exon 47 variants might specifically modulate these properties.

In conclusion, our results exclude a functional role of the C-terminal fragments on Cav2.1 expression level and electrophysiological properties when expressed in X. laevis oocytes.

Therefore, we could hypothesize that Cav2.1 C-terminus is mainly involved in Cav2.1 trafficking to specific cellular compartments. Moreover, we found that the C-terminal fragments produced by the Cav2.1 transcripts may contain different splice variants, especially in exon 47 sequence. These fragments, produced thanks to the presence of an IRES, regulate the expression of specific genes [START_REF] Du | Second cistron in CACNA1A gene encodes a transcription factor mediating cerebellar development and SCA6[END_REF]. Interestingly, the pathological pQ expansion found in patients with SCA6 abolishes the transcriptional activity of 1ACT. Thus, the transcriptional roles of the C-terminal fragments in mouse, rat and rabbit Cav2.1, or of human Cav2.1 without pQ expansion but with a specific C-terminal sequence need to be investigated. Cav21. The parameters of both curves and the R400values are similar or marginally affected (see Table 1). n.s = non-significant. (c) The percentage of current recovery after 8 s of Cav2.1 is significantly smaller than that of Cav2.1(e47) (86 ± 1 %, n = 9 and 89 ± 1 %, n = 10, respectively; p = 0.0036) and is similar to that of Cav2.1(e47s) (85 ± 1 %, n = 6; p = 0.49). n.s = non-significant. values (right) obtained for Cav2.1 alone, or with CT2-6His or CT2(e47)-6His. The parameters of both curves and the R400 values are similar or marginally affected (see Table 1).

FIGURE CAPTIONS Figure 1 .

 CAPTIONS1 FIGURE CAPTIONS Figure 1. Summary of the events leading to the expression of Cav2.1 isoforms with different C-terminus sequences in the rat and mouse. (a) The use of an alternative acceptor site at the exon 46-47 boundary allows the insertion of the GGCAG sequence at the beginning of exon 47 and the in-frame translation of exon 47, leading to Cav2.1 isoforms with full-length or deleted exon 47 in the rat (AAC24516, TCA50, CAJ09863, TCA56) and mouse (AAW56205, BAB85612, BAB85611). Conversely, omission of the GGCAG sequence causes a frameshift, leading to a stop codon at the beginning of exon 47 (AAC52940, AAC40806) (b) Alignment of the amino acid sequences of the extended C-terminus of rat and mouse Cav2.1.

Figure 2 .

 2 Figure 2. The short and the two long rat Cav2.1 variants show similar functional properties. (a) Schematic representation of the three rat Cav2.1, Cav2.1(e47) and Cav2.1(e47s) variants that differ at the C-terminus. The arrow indicates where the translation of human 1ACT begins

Figure 3 .

 3 Figure 3. The rat Cav2.1, Cav2.1(e47) and Cav2.1(e47s) variants exhibit similar recovery from inactivation. (a) Representative current traces obtained in X. laevis oocytes that co-express the

Figure 4 .

 4 Figure 4. Rat Cav2.1 variants produce C-terminal fragments that interact with Cav4a. (a)

Figure 5 .

 5 Figure 5. Over-expression of C-terminal fragments does not affect Cav2.1 expression and

Figure 6 .

 6 Figure 6. Deletion of the Cav binding domain in Cav2.1 C-terminus does not affect Cav2.1

Table 1 :

 1 Parameters of the voltage-current curves and of the inactivation curves, and R400 values for all the subunit combinations (Cav21 was always present). Data are the mean ± S.E.M. of n oocytes. Statistical significance (p values using Student's t test) vs Cav2.14, p; vs Cav2.11b, *p; vs Cav2.12a, **p; vs Cav2.1C24, ***p, is given for all parameters.

			Current voltage curves			Inactivation curves
		Va (mV)	ka (mV)	Erev (mV)	n	Vi (mV)	ki (mV)	R	n	R400	n
	Cav2.14	-9.0±1.0	-3.2±0.3	54±1	31	-22.9±0.7	7.1±0.2	0.14±0.01	26 0.43±0.02 33
	Cav2.14CT1	-8.2±1.2	-4.5±0.2	51±1	24	-26.0±0.9	8.0±0.3	0.15±0.02	14	0.45±0.03	22
		p=0.62	p=0.0005	p=0.0146		p=0.0094	p=0.0402	p=0.71		p=0.35
	Cav2.14CT1 (1:5)	-11.9±1.4	-3.2±0.4	50±1	11	-28.2±0.6	6.7±0.2	0.12±0.02	7	0.49±0.04	12
		p=0.1056	p=0.99	p=0.60		p=0.0003	p=0.37	p=0.62		p=0.1185
	Cav2.14CT1(e47)	-8.6±1.3	-4.1±0.3	53±1	28	-27.4±1.0	8.6±0.3	0.09±0.02	21	0.40±0.03
		p=0.65	p=0.0077	p=0.37		p=0.0003	p=0.0004	p=0.0376		p=0.55

ACKNOWLEDGEMENTS

The authors wish to acknowledge the Centre National de la Recherche Scientifique, the Institut National de la Santé et de la Recherche Médicale and the Université de Montpellier for their financial support. T.C. wishes to acknowledge Naouel Ougouti for her implication at the beginning of this work.

COMPETING INTERESTS

The authors declare no competing interest.

AUTHORS' CONTRIBUTIONS

TC designed the study, performed the molecular biology and the electrophysiological experiments, analyzed the results and wrote most of the paper. TC and CM did the Western blotting and the co-immunoprecipitation experiments. PC, MR and MV provided guidance and support, and critically reviewed the manuscript.

DATA ACCESSIBILITY

The authors confirm that all data that led to the findings are available and will be shared with the research community upon request to the corresponding author. Please direct all email requests to thierry.cens@inserm.fr. ABBREVIATIONS polyQ -tract of glutamine residues IRES -internal ribosome entry site SCA6 -spinocerebellar ataxia type 6 X. laevis -Xenopus laevis


Olivier Ferret
email: ferret@limsi.fr

Brigitte Grau
email: grau@limsi.fr

Gabriel Illouz
email: gabrieli@limsi.fr

Christian Jacquemin
email: jacquemin@limsi.fr

Nicolas Masson
email: masson@limsi.fr

QALC -the Question-Answering program of the Language and Cognition group at LIMSI-CNRS

Introduction

In this report we describe the QALC system the Question-Answering program of the Language and Cognition group at LIMSI-CNRS which has been involved in the QA-track e v aluation at TREC8. The purpose of the Question-Answering track is to nd the answers to a set of 200 questions. The answers are text sequences extracted from the volumes 4 and 5 of the TREC collection. All the questions have at least one answer in the collection.

The basic architecture of QALC is composed of ve parallel modules, two for the questions and three for the corpora, and a sixth pairing module which produces the sentences ranked by decreasing order of relevance. Figure 1 illustrates the main components of QALC . More details about each component will be given in the gures corresponding to the detailed descriptions of these subparts.

The QALC system relies mainly on genuine Natural Language Processing components. Most of the components rely on a tagged version of the corpus. We use the TreeTagger for this purpose [START_REF] Stein | Etiquetage morphologique de textes fran ais avec un arbre de d cision[END_REF] The system is based on the following six modules:

Natural language question analysis The analysis of the questions relies on a shallow parser which spots discriminant patterns and assigns categories to the questions. The categories correspond to the types of entities which are likely to constitute the answer to this question.

Term extraction The term extractor is based on syntactic patterns which describe compound nouns. The maximal extension of these compounds is produced along with the plausible subphrases.

Automatic indexing & variant con ation Automatic indexing relies on FASTR [START_REF] Jacquemin | Syntagmatic and paradigmatic representations of term variation[END_REF] shallow transformational natural language analyzer which recognizes the occurrences and the variants of the terms produced by the preceding module. Each occurrence or variant constitutes an index to the document which is ultimately used in the process of document ranking and in the process of question document pairing.

Named entity recognition Similarly, named entities are recognized in the documents in order to build indices which are used for measuring the degree of similarity b e t ween the questions and the document sentences. Named entities are extracted through lexico-syntactic patterns combined with signi cantly large lexical data.

Document ranking & thresholding

The last two modules are information retrieval modules as opposed to the four preceding components. Documents are ranked according to a weighted measure of the indices produced by the automatic indexing and variant con ation module. Only the n best ranked documents are selected. A further selection of the documents is made if a plateau can be recognized in the relevance curve of the documents. Question sentence pairing Finally, all the data extracted from the questions and the documents by the preceding modules is used by a pairing module to evaluate the degree of similarity b e t ween a document sentence and a question. The sentences are chosen from the documents selected by the preceding module. The following sections present in detail the modules of the QALC system.

Natural Language Question Analysis

Question analysis is performed in order to assign features to questions and use these features for the pairing measurement b e t ween a query question and potential answer sentences answer. Basically, question analysis allows the prediction of the kinds of answer, called target for instance, ORGANIZATION. The retrieved documents see Section 4.2 are processed in order to recognize the named entities. Named entities are labeled with the same set of tags as the questions. During the pairing measurement, the more the question and a sentence share the same tags, the more they are considered as involved in a question-answer relation.

Example:

Question: How many people live in the Falklands? ! target = NUMBER Answer: ... Falklands population of b_numex_TYPE=NUMBER 2,100 e_numex is concentrated. The last two t ypes entail the analysis of How-questions. TYPE 5 leads the system to choose a target according to the semantic category of the adjective, and, in TYPE 6 questions, the choice relies on the semantic category of the NP. TYPE 5: how AdjSem. TYPE 6: how much many NPsem.

Target Set

Our parser rst tries to apply these rules on the beginning of the sentence. If none of them is red, the parser tries to nd one of these patterns inside the sentence, as in the sentence The Faroes are p art of what northern European country? Some rules lead to nd several targets in case of ambiguity. F or example, the target of the question Where is Bolivia? may be either a LOCATION-STATE or a LOCATION-CITY.

Semantic Categories

Each semantic category corresponds to a target. The category of a noun phrase is based on the semantic class of its main noun. A noun phrase is made of a determiner DET, followed by successive adjectives JJ, nouns N with a possible possessive case Poss, or verbs at the gerundive V BG or past participle V BD form. We h a ve decided to search for the largest noun phrase structure: DET JJjN Poss jNjV BG jV BD ? N 1 The head of a noun phrase is the last noun of the longest match. For example, in Johnny Mathis' high school track coach, coach is recognized as the head. We h a ve established 17 semantic classes, hierarchically structured as shown in Figure 2. What is the number of bu aloes ! NUMBER What two companies ... ! ORGANIZATION What date year ! DATE Which country ! STATE What is the world record time ! TIME What is the capital of ... ! CITY Some adjectives used with how, a s tall, long, etc., are also classi ed in semantic classes that belong to the sub-tree representing numeric entities.

Term Extraction

The relevant documents are retrieved through a pairing procedure which relies on a measure of similarity. The similarity b e t ween a document and a query, detailed in Section 6, is computed on the basis of common terms and on the correspondence between the tags assigned to the question and the named entities extracted from the sentences. Terms are extracted from the questions and sentences are indexed by these terms. In this section, we describe the acquisition of terms from the questions. In the next section, the process of indexing sentences with these terms is detailed.

As for automatic acquisition of terms from questions, we use a simple technique of ltering through patterns of part-of-speech categories. No statistical ranking is possible because of the small size of the questions from which terms are extracted.

The questions are rst tagged with the help of the TreeTagger. Then, patterns of syntactic categories are used to extract terms from the tagged corpora. They are very close to those in Justeson and Katz, 1995, but we do not include post-posed prepositional phrases. The pattern used for extracting terms is1 :

JJjN N jN P jV BG ?JJjN N jN P jV BG N P jN N jV BD jN N jN P jCD 2 The longest string is acquired rst and substrings can only be acquired if they do not begin at the same word as the superstring. For instance, from the sequence name NN of IN the DT US NP helicopter NN pilot NN shot VBD down RP , the following four terms are acquired: US helicopter pilot, helicopter pilot, pilot, and shoot.

The mode of acquisition chosen for terms amounts to considering only the substructures that correspond to an attachment of modi ers to the leftmost constituents the closest one. For instance, the decomposition of US helicopter pilot into helicopter pilot and pilot is equivalent to extracting the subconstituents of the structure US helicopter pilot .

Automatic Indexing and Document Ranking

The selection of relevant documents relies on an NLP-based indexing composed of both single-word and phrase indices and linguistic links between the occurrences and the original terms. The original terms are those extracted from the questions and presented in Section 3. The tool used for extracting text sequences that correspond to occurrences or variants of these terms is FASTR [START_REF] Jacquemin | Syntagmatic and paradigmatic representations of term variation[END_REF]. The ranking of the documents relies on a weighted combination of the terms and variants extracted from the documents.

NLP-based Indexing through FASTR

The automatic indexing of documents is performed by FASTR , a transformational shallow parser for the recognition of term occurrences and variants. The terms acquired in Section 3 are transformed into grammar rules and the single words building these terms are extracted and linked to their morphological and semantic families.

The morphological family of a single word w is the set Mw of terms in the CELEX database CELEX, 1998 which h a ve the same root morpheme as w. F or instance, the morphological family of the noun maker is made of the nouns maker, make and remake, and the verbs to make and to remake.

The semantic family of a single word w is the union Sw of the synsets of WordNet1.6 Fellbaum, 1998 to which w belongs. A synset is a set of words which are synonymous for at least one of their meanings. Thus, the semantic family of a word w is the set of the words w 0 such that w 0 is considered as a synonym of one of the meanings of w. The semantic family of maker, obtained from WordNet1.6, is composed of three nouns: fmaker, manufacturer, shaperg and the semantic family of car is fcar, auto, automobile, machine, motorcarg.

Variant patterns that rely on morphological and semantic families are generated through metarules. They are used to extract terms and variants from the document sentences in the TREC corpus. The following pattern 2 extracts the occurrence making many automobiles as a variant of the term car maker:

V M 0 maker 0 R P ? PREP ? ART N N jN P ? PREP ? ART ? JJ j N N j N P j V BD j V BG 0,3 N S 0 car 0 3 V M 0 maker 0 is any v erb in the morphological family of the noun maker and N S 0 car 0 is any noun in the semantic family of car.

Relying on the above morphological and semantic families, auto maker, auto parts maker, car manufacturer, make autos, and making many automobiles are extracted as correct variants of the original term car maker through the metarule set used for the QA-track experiment. Unfortunately, some incorrect variants are extracted as well, such a s make those cuts in auto produced by the preceding metarule.

Document Ranking

The output of NLP-based indexing is a list of term occurrences composed of a document identi er d, a term identi era pair tq;i composed of a question number q and a unique index i, a text sequence, and a variation identi er v a metarule. For instance, the following index: LA092690-0038 t131; 1 making many automobiles NtoVSemArg 4 means that the occurrence making many automobiles from document d =LA092690-0038 is obtained as a variant of term i = 1 in question q = 131 car maker through the variation given in Section 4.1.

2 RP are adverbs, PREP prepositions, ART articles, and V verbs. Each document d in the collection is associated with a vector Wd = W q d q2f1;:::;200g of 200 weights, one for each question q. The weighting scheme relies on a measure of quality of the di erent families of variations described in Jacquemin, 1999: non-variant occurrences are weighted 3.0, morphological and morphosyntactic variants are weighted 2.0, and semantic and morpho-syntactico-semantic variants are weighted 1.0.

Since proper names are more reliable indices than common names, each term tq;i receives a weight Ptq;i between 0 and 1.0 corresponding to its proportion of proper names. For instance, President Cleveland's wife is weighted 2 3 = 0 :66. Since another factor of reliability is the length of terms, a factor jtq;ij in the weighting formula denotes the number of words in term tq;i.

The weight W q d of a query q in a document d is given by the following formula 5. The products of the weightings of each term extracted by the indexer are summed over the indices Id extracted from document d and normalized according to the number of terms jT qj in query q.

W q d = X tq;i;v2Id wv 1 + 2 P tq;i j tq;ij jT qj 5

For each query q, the 100 best ranked documents are retrieved. Mainly two t ypes of weighting curves are observed for the retrieved documents: curves with a plateau and a sharp slope at a given threshold Figure 3.a and curves with a slightly decreasing weight Figure 3.b. The edge of a plateau is detected by examining simultaneously the relative decrease of the slope with respect to the preceding one, and the relative decrease of the value with respect to the preceding one. if W q d 2 W q d 1 0:5 then i 0 = 2 6 else i 0 = min i 2 f 3; : : : ; 100g W q d i , W q d i,1 W q d i,1 , W q d i,2 0:5 ^Wq d i W q d i,1 0:8 f 100g 7

Through this method, the threshold i 0 is 8 for question 87 Who followed Willy Brandt as chancellor of the Federal Republic of Germany?, Figure 3.a and 100 for question 86 Who won two gold medals in skiing in the Olympic Games in Calgary?, Figure 3.b. As indicated by Figure 3.a, there is an important di erence of weight b e t ween documents 8 and 9. The weight of document 8 is 9.57 while the weight o f document 9 is 7.29 because the term Federal Republic only exists in document 8. This term has a higher weight because it is composed of two proper names.

The i 0 best ranked documents are then processed by the question sentence pairing module presented in Section 6. At the document level, this module relies on single words, term indices, and named entities. Term indices have been presented in this section, named entities are presented in the next one.

Named entities are recognized in the documents in order to build indices which are used for measuring the degree of similarity b e t ween the questions and the document sentences. Named entities receive one of the following types: PERSON, ORGANIZATION, LOCATION, NUMBER. They are de ned in a similar way to the MUC task Grishman and Sundheim, 1995 and recognized through a combination of lexical lookup for syntactic or semantic tags on the single words and rules which use these tags together with lexical elements; and dictionary lookup the direct access to lists of named entities. The three lists used for lexical lookup are CELEX CELEX, 1998, a lexicon of 160,595 in ected words with associated lemma and syntactic category, a list of 8,070 rst names 6,763 of which are from the CLR CLR, 1998 archive at New Mexico State University and a list of 211,587 family names from the CLR archive at New Mexico State University.

Numeric Entities

This category groups all kinds of number formulations, time expressions, and specialization of numbers according to their units, even if they are not expressed with numbers. Numeric entities are given a tag among FINANCIAL-AMOUNT, LENGTH, VOLUME, WEIGHT, PHYSICS, DATE, PERIOD, and NUMBER.

The recognition of numeric entities is performed in three steps: Firstly, w e recognize basic entities as cardinal and ordinal numbers, either written with digits or with letters.

In a second stage, we apply rules in order to recognize complex numeric entities such as monetary amounts, distances, or weights. In a third stage, we apply rules in order to recognize time expressions labeled as TIMEX such a s dates, times, ages and periods. These rules use the output of the rst set of rules dedicated to basic numeric entities such as cardinal and ordinal numbers written in digits or letters. In a nal stage, all basic numeric entities that have not been included into a complex entity are tagged as NUMBER.

Organizations, Persons and Locations

A list of 22,095 companies from the Wall Street Research Network and 649 organization obtained through lexical acquisition from the Internet is used to spot organization names. In addition, a set of rules recognizes organizations which are noun phrases that either begin with a speci c modi er such a s Christian, Democratic, Federal... or have a speci c head word such a s Academy, Administration, Association.... A w ord which belongs to the list of proper names exploited during the lexical lookup is tagged as a person. In addition, pairs of capitalized words that begin with a rst name, or triples of capitalized words that begin with a title such a s Dr, President, Ayatollah... are tagged as persons.

Simple anaphora which correspond to the elision of a subpart of a proper name are handled by the recognizer: a rule states that all the occurrences of a hnamei inside a single document are references to the person identi ed by the rule hfirstNamei h namei.

The location recognition module uses two lexical ressources from the CLR: a list of 7,813 city names and a list of 1,144 country names. No rules have been written for these entities so far. This section presents the module that selects for each question a list of 5 ranked responses that are no longer than 250 characters each. This module relies on the results of all the preceding modules: each question is assigned a set of terms and one or several categories according to its focus; a set of documents is selected for each question. In each of them, named entities and terms extracted from the questions are tagged.

General Principles

The Question Sentence Pairing module relies on two main choices, close to those that support the preliminary version of the Deep Read system [START_REF] Hirschman | Deep read: A reading comprehension system[END_REF] Firstly, w e take the sentence unit as our basic unit for the answers to the questions, given that the QALC system aims at nding precise information that can be expressed in a single clause. Secondly, the Question Sentence Pairing module directly searches for the possible answers of a question in all the documents selected by the Document Ranking & Thresholding module without trying to delimit smaller units. Such a c hoice allows the scanning of a large set of possible answers without too high a cost.

Considering the two preceding choices, the Question Sentence Pairing module is based on a very simple principle: we compare each sentence from the selected documents for a question to this question and we always keep the ve sentences that are the most similar to the question. This comparison is done rst by transforming both the question and each document sentence into vectors and then, by computing a similarity measure between these two v ectors. As is usual in the eld of Information Retrieval, this similarity measure basically relies on the words of both the question and the sentences from the documents. But in our case, it also takes into account and merges all the linguistic information from the preceding modules. The nal step of the module consists of cutting down the sentences that are longer than 250 characters. This is done here by simply removing the rst and the last characters until we reach the xed length.

Basic Similarity Measure

The similarity e v aluation starts by turning sentences questions and document sentences into vectors of words. Such a v ector only contains the most signi cant w ords of the primary sentence, i.e. mainly its content w ords: nouns and proper nouns, verbs, adjectives including comparatives and superlatives, adverbs including superlatives and comparatives, foreign words, symbols and cardinal numbers. These words are lemmatized and we take their canonical form as a reference in the vectors. We also have a short stop-list in order to remove some frequent w ords that are selected according to the previous criterion but that are not meaningful in our task. All this selection process relies on the morpho-syntactic tagging done by the TreeTagger, which also performs the lemmatizing.

Each w ord in a vector is weighted according to its importance in relation to the Question Answering corpus. This importance is evaluated by using the tf.idf weighting policy, as it is often done in Information Retrieval. Word order in vectors is not signi cant because our similaritymeasure does not take this parameter into account. We think that it is too restrictive a constraint considering the kind of processing we do.

The similarity measure between a vector representing a question, V q , and a vector representing a document sentence, V d , is given by: simV q ; V d = P i wd i P j wq j 8 with wq j , the weight o f a w ord in the question vector and wd i , the weight o f a w ord in a sentence vector that is also in the question vector. This measure evaluates the proportion and the importance of the words in the question vector that are present in the sentence vector with regard to all the words in the question vector. It is not symmetrical: it favors the question point of view. It focuses on the similarities between the questions and the document sentences because rstly, there are too many di erences at this level in comparison with the similarities, and then, these di erences are globally not relevant.

On the other hand, we take i n to account the di erence in length between a question and a document sentence. This criterion is used as a secondary key for sorting the sentences that are selected as possible answers to a question : if two sentences have the same similarity v alue, we sort as rst the sentence that has the closest length with regard to the question.

Similarity Measure with Linguistic Features

We h a ve c hosen to take i n to account the results of the previous modules without changing our basic mechanism. Two kinds of linguistic features are considered: terms and named entities. Both of them are added to the vectors as if they were new signi cant w ords.

Terms Each of the terms extracted from a question has a unique identi er, which is used for marking the occurrences and the variants of this term both in the questions and in the document sentences. In the sentences, this identi er is associated with a score that re ects the distance between the found variant and its reference form in the question see Section 4.2. In a question vector, we add the term identi er with a default weight of 0.5. In a sentence vector, we add the identi er of the recognized terms with a weight equal to the score of the variant divided by the maximum possible score.

Named entities Each recognized named entity is marked with a speci c tag according to its type see Section 5. On the other hand, the kind of the answer expected for each question is determined by the Question Analysis module. Thus, for a question, we add the tags of the expected types of the answer to its vector and for a sentence, we add the tags of the named entities that have been recognized in the sentence to its vector. In both cases, each tag is given a xed weight, which is set to 0.5.

The weights associated with the term identi ers and with the named entity tags have been experimentally set. They are globally lower than the weights of the words. Thus, as for the di erence of length, these linguistic features are used as a secondary criterion in the similarity measure. They help in increasing the rank of an answer which has already been selected, but the selection of the possible answers is mainly based on the number and the importance of the words shared by the question and these answers.

Results

Our o cial results are the following: our mean reciprocal rank is 0.341, with 88 questions answered. More precisely, w e got 56 answers at rank 1, 12 at rank 2, 9 at rank 3, 7 at rank 4 and 4 at rank 5. Globally, this means that when an answer is found, it often appears on top position. Since our sentence truncating procedure for producing 250 character answers is very rough, our system sometimes missed the correct answer although it found a correct sentence. We think that applying very simple heuristics based on the recognized named entities could easily solve this problem.

One important aspect of the Question Answering task is the ability to determine the expected type of answer i.e. the target in order to match it with named entities that are recognized in documents. Hence, we h a ve analyzed our results according to this criterion. Among the 198 questions, a target was identi ed for 162 of them. The following table shows the percentage of correct answers in relation to the target type. These results lead us to conclude that the recognition of a target does not in uence the system's ability to nd a correct answer, which seems rather contradictory with the main trends of the best participants. However our results for numbers, that are easily recognizable, comply with these trends and suggest that we h a ve to enhance our named entity recognition module. We also think that our pairing module takes into account t o o w eakly the concordance between the targets of questions and the named entities of documents.

Conclusion and Future Developments

Since the LIMSI laboratory did not have experience in the development of Question-Answering system before participating in the QA-track this year, many research issues have arisen from the construction of our system. Among the future developments that we are considering for our next participation in the QA-track are:

exploring di erent w eighting schemes for the computation of the similarity of a question and a sentence. For instance, when pairing a sentence with a question requiring a date, named entities denoting a date in sentences should be weighted higher; answer unit could be enlarged and position of indices inside a document could be accounted for in order to focus on the units that gather the largest number of indices and which are more likely to provide the answer; cascaded indexing with FASTR would optimize the computation by running FASTR on a smaller part of the collection; term acquisition could be improved through a disambiguation of long noun phrases and a better partof-speech tagging of the questions; named entity recognition could be improved through machine learning techniques [START_REF] Baluja | Applying machine learning for high performance named-entity extraction[END_REF]

Figure 1 :

 1 Figure 1: Flowchart of QALC.

Figure 2 :

 2 Figure 2: The semantic classes

Figure 3 :

 3 Figure 3: Two t ypes of weighting curve.

  The targets used are PERSON, ORGANIZATION, LOCATION either CITY or PLACE, TIME-EXPRESSION either DATE, TIME, AGE or PERIOD, and NUMBER either LENGTH, VOLUME, DISTANCE, WEIGHT, PHYSICS or FINANCIAL. Some examples of sentences which can be associated with targets follow. Elements of sentences called triggers which are relevant to assign a given target are underlined.

	PERSON: Who was the rst President of the USA?
	ORGANIZATION: What laboratory discovered the AIDS virus?
	LOCATION:
	PLACE: What is the longest river in Asia? What is the name of the highest mountain in the world?
	TIME-EXPRESSION:
	PERIOD: During which period did the dinosaurs vanish?
	2.2 Types of Questions
	Question analysis is performed by a specialized shallow parser. It is based on lexical, syntactic and semantic knowledge, i.e. some speci c words, syntactic categories, grammar rules and semantic classes for nouns. We have identi ed six kinds of formulations for questions allowing the prediction of the kind of answer.
	TYPE 1: the answer only depends on the interrogative pronoun. It is the case with who whom whose, where and when.
	The following three patterns are dedicated to parse Which What-questions. These patterns are identical whatever kind of answer is expected. Disambiguation is provided by the semantic class of the head noun belonging to the noun phrase NPsem. These classes are detailed in section 2.3.
	TYPE 2: what which ...be NPsem
	TYPE 3: what which NPsem. A variant of this last rule is TYPE 3b, for questions about time. TYPE 3b: PREP what which NPtime
	TYPE 4: what which is the name of NPsem.

N N are common nouns, N P proper nouns and CD numeral determiners.