Lionel Tadonfouet Tadjou

Fabrice Bourge

Tiphaine Marie
email: tiphaine.marie@orange.comlaurent.romary

Laurent Romary

Eric De

La Clergerie
email: eric.delaclergerie@inria.fr

Building A Corporate Corpus For Threads Constitution

In this paper we describe the process of building a corporate corpus that will be used as a reference for modelling and computing threads from conversations generated using communication and collaboration tools. The overall goal of the reconstruction of threads is to be able to provide value to the collorator in various use cases, such as higlighting the important parts of a running discussion, reviewing the upcoming commitments or deadlines, etc.

Since, to our knowledge, there is no available corporate corpus for the French language which could allow us to address this problem of thread constitution, we present here a method for building such corpora including different aspects and steps which allowed the creation of a pipeline to pseudo-anonymise data. Such a pipeline is a response to the constraints induced by the General Data Protection Regulation GDPR 1 in Europe and the compliance to the secrecy of correspondence.

Introduction

Computer mediated communication (CMC) tools are used to produce and exchange an enormous amount of contents such as emails, chats and fora. But these contents are usually little or not structured, which makes the automatic extraction of knowledge difficult. In this paper, we tackle the issue of the constitution of threads in conversations within a specific company. This problem strongly relates to that of conversations disentanglement which have been addressed in several research works. [START_REF] Jiang | Learning to disentangle interleaved conversational threads with a Siamese hierarchical network and similarity ranking[END_REF] computed message similarity using language representation to address this problem. Such problems requires the existence of a corpus to analyse. It is why [START_REF] Kummerfeld | A large-scale corpus for conversation disentanglement[END_REF] have built a large-scale corpus for conversations disentanglement, also the EN-RON corpus was created by [START_REF] Klimt | The enron corpus: A new dataset for email classification research[END_REF] for email classification research. In the same way [START_REF] Chanier | The CoM-eRe corpus for French: structuring and annotating heterogeneous CMC genres[END_REF] built the CoMeRe corpus for French and [START_REF] Douglas | Avocado research email collection[END_REF] created the Avocado Research Email Collection. More recently [START_REF] Bevendorff | Crawling and preprocessing mailing lists at scale for dialog analysis[END_REF] built the Webis Gmane Email Corpus from the archive of Gmane mailing list. Among these corpora, some are from ENRON and AVOCADO companies, but they are in English and their content have specific contexts different from those of the company of which we will create a corpus. Those that are in French have very few emails around 2030 (CoMeRe) or are mailing list conversations with themes and context that do not fit with corporate conversations. One approach could be transfer learning, [START_REF] Bornea | Multilingual transfer learning for qa using translation as data augmentation[END_REF] use multilingual transfer learning for question answering problem. But the context of a company is very circumscribed and specific.

In this paper we describe a method for building a corporate corpus based on emails data. We specifically address the process of collecting, preprocessing and pseudo-anonymizing emails in order to meet legal, personal, acceptability and social constraints. This comprises the use of models for Named Entity Recognition (NER), rule-based systems and their evaluation. In the upcoming sections, we will present research works on corpus building and constraints related to it.

Related Work

Building a corpus is a prerequisite step to various research work in different fields such as discourse analysis, automatic translation and transcription, image recognition, emails segmentation, named entities recognition, etc. [START_REF] Chanier | The CoM-eRe corpus for French: structuring and annotating heterogeneous CMC genres[END_REF] built the CoMeRe corpus for discourse analysis and carried out linguistic studies of idiolects that appear in different types of CMC. The CoMeRe corpus consists of heterogeneous CMC in French including 3 million chats, 44K SMS, 2,030 emails, 2,700 forum messages and 34k Tweets.

Automatically classifying emails in specific email directories and extracting information from them are issues that led to the creation of the EN-RON Corpus by [START_REF] Klimt | The enron corpus: A new dataset for email classification research[END_REF], a corpus of emails in English from a private company. The ENRON corpus has been widely used: email formalities in work environments are studied by [START_REF] Peterson | Email formality in the workplace: A case study on the Enron corpus[END_REF], also [START_REF] Chanier | The CoM-eRe corpus for French: structuring and annotating heterogeneous CMC genres[END_REF] quantify the feelings and tone used in emails.

In order to disentangle conversations in the same message stream, [START_REF] Kummerfeld | A large-scale corpus for conversation disentanglement[END_REF] developed a large corpus of 77,563 messages including 74,963 from Ubuntu's Internet Relay Chat (IRC) channel and 2,600 messages from Linux's that highly contribute to research in dialogue analysis.

Recently, [START_REF] Bevendorff | Crawling and preprocessing mailing lists at scale for dialog analysis[END_REF] developed the largest corpus available named Webis Gmane Email Corpus 2019 consisting of 153 million emails extracted from 14,669 mailing lists. This corpus data exists in three major languages: English, German and French. These emails are segmented into 15 classes of emails by a neural model with a performance of 96%.

As a whole, the scarcity of reference data in general and in particular for the French language justifies our approach to build a corpus that covers the need of thread analysis in CMC context.

Constraints: Legal, personal and psychological

Certain conditions govern the exploitation of public data, including forum, mailing list or web site data. These types of conditions are generally displayed to and agreed upon by the user when registering for these forums and or mailing lists. Mentions contained in these terms and conditions often refer to the exploitation of user data for the purposes of training, service improvements and or advertising. Unlike this process of joining forums, mailing lists or websites, employees in corporate do not always have to explicitly validate such conditions, but are required to keep secret certain company information on the one hand and on the other hand the private information exchanged in corporate via CMC are governed by the secrecy of correspondence and the General Data Protection Regulation (GDPR) adopted in 2016 and became effective on 25 May 2018 in the European union.

Personal data protection concerns have been elicited since 1970 [START_REF] Hallinan | Opinions can be incorrect (in our opinion)! On data protection law's accuracy principle[END_REF], with Ann Cavoukain, the Privacy Commissioner of Ontario, Canada, co-author of the 1995 international report on Privacy Enhancing Technologies (PET) acting as a forerunner in the domain of data privacy in any technological design. [START_REF] Cavoukian | Privacy by design: The definitive workshop. a foreword by ann cavoukian, ph.d[END_REF] further clarified that "the future of privacy cannot be assured solely by compliance with regulatory frameworks; rather, privacy assurance must ideally become an organization's default mode of operation".

With regard to the secrecy of correspondence, it would be a violation if a third party managed to access even a conversation of collaborators without their consent. Our goal of disentangling forum conversations, chats and corporate emails could only be carried out under the respect of the secrecy of correspondence. Thus obliging us to submit consent requests to the various collaborators involved in these conversations. This is a tedious task given the number of collaborators in the different teams or entities in a company.

In the same vein, the GDPR was established in the European Union territory to protect personal data. It applies to any organisation, public and private, that processes personal data on its behalf or not, provided that it is established in the territory of the European Union, or that its activity directly targets European residents. The GDPR strengthens the obligation of information and transparency towards the persons (collaborators in our case) whose data we process. Despite this obligation of transparency towards collaborators, they are able to exercise their rights of refusal, withdrawal (if committed beforehand) of exploitation of their data. Secure and substitute some information from this data are part of the conditions to be respected in order to be GDPR compliance. These conditions are binding for the data acquisition process because require to set up stable and streamlined processes that include administrative and material aspects.

The two previous forms of constraints put collaborators at the centre of any data acquisition process in a company. They need to know why and how their data will be used. And depending on the answers given to them, they are free to give their consent or not. Unwillingness to share their content which potentially includes personal or highly sensitive data, brings out another group of con-straints that are personal, acceptability and even psychological.

All these constraints need to be addressed when dealing with business data acquisition and processing.

Methodology and Implementation

Our process is based upon three main stages: collecting conversations, preprocessing them and pseudo-anonymizing sensitive information in these conversations. For the global aim of conversation disentanglement, it is important to mention that the data will be further analysed by a limited number of people from two different teams, one within the company and another the associated public research team. Our goal is to process the data, with anonymization or pseudo-anonymization techniques in particular, so that it is not possible for any person outside that team to identify any participant in the conversations nor to be able to detect participants' personal data. In order to verify our compliance with GDPR and the secrecy of correspondences, our project has gone through an internal validation procedure supervised by a legal commission within the company on the basis of a data life-cycle and a completed risk analysis form. The data life-cycle describes in detail which operations would be performed by which person acting with what role. In particular it helped us identify the possible leaks in the process and their potential consequences. It also specifies that before the anonymization or pseudo-anonymization stage, only specific researcher, who bears the role of technical data controller (TDC) -can access the whole collected data on a secure storage device. The other collaborators in the team can only access the data collected from their own computer. This legal commission evaluated defined the process to follow.

This process consists first in obtaining a consent from the targeted collaborators. Then, once the data has been collected, any text string that directly or indirectly allows identification of a person or access to personal information must either be removed, modified or pseudo-anonymized. It is worth mentioning that the consent provided by the collaborators is valid for a maximum of one year at a time.

To obtain collaborators' consent, we produced media content (video and slides) explaining why and how the process of collecting and analysing data would take place. This is to provide transparency, as requested by GDPR. Obtaining a collaborator's consent means having him/her sign a document explaining why we want to collect emails he/she is involved in and under what conditions it is done. For COVID-19 reasons these documents were signed electronically and stored on a secure file system.

First stage: Collecting conversations

Since our objective was to collect a large corpus of French interactions occurring in the context of computer mediated exchanges in a business environment, we focused specifically on emails since a) these are still widely used in professional context and b) other sources appeared to be more fragmented and difficult to gather in a coherent way. Our main goal is to reconstruct threads from those conversations so that each thread only deals with a clear and well-circumscribed topic. After dealing with emails conversations, we will then explore other types of CMC in corporate such as forums, chats, IM, etc.

Still, the sole gathering of emails can appear to be a painful task. Beyond the difficulty of just gathering users' consents, the resulting issue is to be able to collect those messages or threads that only relate to the collaborators that have actually given their consent since obviously many threads involve several collaborators. Finally, the technical deployment of the mailing environment (in our case: Microsoft's Exchange server) can in itself be a hindrance to the precise querying and retrieval of the appropriate content.

As a consequence, we chose to collect emails directly from the collaborators' desktop computers. This choice was guided by two main reasons, namely:

• It is easier to obtain consents of some targeted collaborators. Aspects of security and confidence are in this way easily approached and collaborators have the full control of what is collected from their computer.

• It also allows us to limit the extraction to relevant emails, thus preventing that we come across, for instance, emails that the collaborator wants to keep confidential.

In order to build a corpus of significant size it is necessary to have the consent of a minimum number of collaborators. However targeting collaborators at random proved not to be sufficient. Indeed since we can only collect emails for which all the collaborators involved gave their consent, we would then take the risk of collecting conversations with many missing emails. This would probably result in having a negative impact when extracting meaningful threads from these amputated conversations.

Our approach was to try to maximise the number of emails/conversations to extract while minimising the number of collaborators to contact. In doing so we distinguished collaborators who gave their consent into two groups, namely: -anchor, are collaborators within our project and with whom we can perform manipulations on their workstation to extract data; -participant, the rest of those collaborators who gave their consent. The above approach is divided into two steps. The first one consists in selecting participant who are"close" to an anchor. The output of this first step is a list of interlocutors that will be used as a guide when selecting potential participant or anchors on other anchor' workstations. The second step is focusing on the actual extraction of emails and conversations on anchor's workstations. During this emails/conversations extraction, we also made sure that we extrated metadata such as senders, receivers, emails and conversations identifiers, dates and times from emails headers. These metadata helped us later for the pseudo-anonymization step and to keep the real links between the different messages of a conversation. To bootstrap this phase we first contacted collaborators from a single team, then from a project, from a larger entity and so on until a substantial amount of data would be collected.

In a more practical way and because Microsoft Outlook is the application used in the company for emails exchanges, we developed a C# Windows Presentation Foundation (WPF)2 application based on Microsoft Office Interface3 that allows us to interact with Outlook application and also with backup files from Outlook. With this tool we can select and unselect folders and contacts from a collbaborator's Outlook mailbox. Once the tool is installed on a anchor's workstation, the anchor uses it with the support of one of the people who designed the tool. Thus it is easy for an anchor to choose the emails folders from which the emails may be extracted and to choose the collaborators that could be contacted to become participants. This gives the possibility to avoid folders with private or personal contents and contacts with whom he/she has confidential exchanges. It also helps to provide data in JSON and CSV formats for future processing.

Until now we have obtained 78 consents from close collaborators involved in shared projects. Based on these 78 consents and as a first stage of data collection, we succeeded in extracting 11K unique emails for a total of 1 023 736 tokens, from the workstations of four anchors (the TDC and three other collaborators of the same team). These emails included conversations dating back to 2013 which shows the extent and richness of the information collected. The current size of this extracted data is 194 MB. These extracted data are only a first part of our final corpus, as we will contact collaborators from other teams and involved in other projects to increase the size of this corpus.

Second stage: Preprocessing collected data

Emails are not structured due to the explosion of different email formats and styles, coupled with the ad hoc ways in which people vary the structure and layout of their messages [START_REF] Vitor | Learning to extract signature and reply lines from email[END_REF]. Emails contain different zones that can be easily identified by humans. [START_REF] Estival | Author profiling for english emails[END_REF] Preprocessing collected data initially consisted in deleting all duplicated emails, followed by identifying, dissociating or removing content from the quoted conversation and boiler-plates zones. To identify and extract quoted messages and signatures from an email, we used Talon4 , a python library. This library is inspired from the research work of [START_REF] Vitor | Learning to extract signature and reply lines from email[END_REF][START_REF] Vitor | Learning to extract signature and reply lines from email[END_REF][START_REF] Joachims | A statistical learning learning model of text classification for support vector machines[END_REF]. As provided, this library works quite well for French e-mails both for the extraction of quoted messages and signatures with an average of predictions of 95% and 70%, respectively, despite the fact that the model within this library has been trained on ENRON corpus, a corpus in English.

Figure 1 shows the large gap between the number of tokens of an email with its quoted messages(in red on the figure) and without them (in blue). These quoted messages are actually the content of all or some previous emails in a conversation that are added to a new email. To comply with GDPR and the secrecy of correspondence, we started by replacing anything in the email body and subject that could indirectly lead to the identification of any person. Here, we were rather in an anonymisation process as we were trying to keep the semantic logic of sentences while substituting expressions. This meant replacing each email address, phone number, URL, path and any user's identifier respectively by @EMAIL, @PHONE, @URL, @PATH and @ID using python's internal regular expression library5 . @PATH and @ID correspond respectively to any absolute or relative path of a folder or file and to sequences of text that uniquely identify the corporate collaborators. All these anonymized sequences do not have a great importance for the problem of thread reconstruction.

Within our collected data, emails are identified by two alphanumeric strings, namely:

• ConversationID: identifies a conversation, so it is the same for all emails within the same conversation

• ConversationIndex: is the unique identifier of an email, it contains its conversation identi-fier (conversationID). It helps to know what the position of an email is in the conversation tree.

We also replace those two strings for each email while keeping emails in their respective conversation and all relationships between emails. This preprocessing step gives us an overview of the substitution method on strings easily identifiable. There are others whose identification is complex and we must find and substitute them. That is why we have conducted experiments of data annotation, Named Entity Recognition (NER) and disambiguation of collaborators names.

Third stage: Entities pseudo-anonymization

Besides indirect content which leads to the identification of a person in emails body, there are strings directly linked to collaborators such as their first names and last names. Emails body may also contain corporate sensitive data or intellectual property such as project names, tools, groups and/or entities names within it, conversation identifiers or any other kind of identifiers. Because of GDPR and secrecy of correspondence compliance, the identification and substitution of these data is necessary. Identify direct strings listed above or sensitive data is become a general problem for a few decades called Named Entity Recognition (NER) in the field of NLP. A new state of art for French Named Entity Recognition has been recently established by Ortiz Suárez et al. ( 2020). However, some prior work has been carried out on NER in context of informal text such as emails. [START_REF] Minkov | Extracting personal names from email: Applying named entity recognition to informal text[END_REF] propose a method based on Conditional Random Fields (CRF) and combined with rule-based system to extract personal names from emails. [START_REF] Zhang | Regular expression guided entity mention mining from noisy web data[END_REF] use regular expressions to collect weak labels from web noisy data for the entity mentions and train a neural network to predict those RE-generated weak labels. These work show that regular expressions, rule-based system are still viable approach to Named Entity Recognition despite good results of recent transformers models which need some fine tuning. As our collected data could not be move on a cloud or GPU server for fine-tuning, we were constraint to approach the problem with a simple computer. We combine several approaches including annotation, CRF, fine-tuned CamemBERT 6 on NER task, regular expressions and rule-based system to achieve our goal of identifying and substitute information to be GDPR compliance.

Data annotation

Building up the ground truth for a downstream NLP task is a necessary step and usually corresponds to annotation which is time consuming especially when it is handcrafted. For NER case, tools have been built to accelerate annotations. Prodigy7 and INCEpTION8 are such tools. These are both accessible via web interfaces. In addition Prodigy offers command-line features. They are all based on active learning (AL) defined by [START_REF] Ren | Understanding interobserver agreement: the kappa statistic[END_REF] as a method that aims to select the most useful samples from the unlabelled data set and hand it over to the "oracle" (e.g., human annotator) for labelling, so as to reduce the cost of labelling as much as possible while still maintaining performance. As prodigy is not free and INCEpTION needs some time to get hands on it, we used a tool built by a former intern of our team. This tool is based on Conditional Random Fields (CRF) and active learning method and as well as being accessible via web interface. From the 11K extracted emails, we selected 1k emails containing at least 142 916 tokens. Three annotators did the annotation exercise on the 1k emails each with Named Entities(NE) tags we defined based on what we considered sensitive information to substitute. Those annotators come from the pool of anchor collaborators from whom we extracted the 11k emails and they annotated a total of 8689 tokens.. They are thus well aware of the general context of the task and therefore ended up being good raters for the task. Table 1 lists the various tags according to the BIO standard. Figure 2 shows the annotation statistics. We can see the very low rate of ID, ROLE, LOC and ORG, which is actually due to the pre-processing stage where we extracted signatures that usually contain identifiers (which have been annotated as ID), roles or functions, phone number, and addresses. The very low rate of phone number is also related to the pre-processing stage described in section 4.2 with the use of regular expressions to identify and substitute phone number, email address, url and path (which sometimes contains the company ID of employees). Also UNCERTAIN is low because raters know well the context of emails that they annotated, otherwise we would have had a high number of UNCERTAIN tags. This shared knowledge of the context of annotated emails is also observed on several tags (LOC, ID, TOOL, PERSON) which are not very disproportionate for raters 1 and 2. Rater 3 annotated more PROJECT and TOOL than two others raters, this shows some ambiguity on strings related to those two tags. This effectively reflects the reality of distinction between project and tool because sometimes in a company, a tool developed in a project can bear the same name of the project. There is also such ambiguity between SUBGROUP (for departments or teams in the company) and PROJECT, due to the fact that sometimes a team name is identical to the product being developed. From the 8689 annotated tokens, we identified 236 different annotations among the three raters, in which the SUBGROUP, PROJECT and TOOL categories are predominant. These disparities show that certain concepts in corporate context are not accurate and may be subject to mis-understanding.

For data annotation, we evaluated inter-annotator agreements (IAA) by means of the standard Cohen Kappa measure McHugh (2012) from the biomedical field. Cohen's Kappa measure is not the most relevant for NER as mentioned e.g. in [START_REF] Hripcsak | Agreement, the f-measure, and reliability in information retrieval[END_REF]; [START_REF] Grouin | Proposal for an extension of traditional named entities: From guidelines to evaluation, an overview[END_REF], because it requires negative cases that do not exist for NER. Also Cohen's Kappa measure can not be used when there is more than two annotators. Since there are three raters for our data, we use Cohen's Kappa to evaluate pairs of raters (R 1 , R 2 ), (R 1 , R 3 ) and (R 2 , R 3 ). To evaluate nominal interannotator agreements (IAA) with more than two annotators, [START_REF] Zapf | Measuring inter-rater reliability for nominal data -which coefficients and confidence intervals are appropriate?[END_REF] advise to use Fleiss's Kappa or Krippendorff's alpha. They study different cases and show that these measures are similar. Annotated data is very unbalanced because of the un-annotated tokens labelled with "O" which are highly represented compared to all existing tokens. As Brandsen et al. ( 2020) did, we computed all IAA scores in both cases with all tokens and only with annotated tokens. Table 2 shows different scores computed, and we observe that the scores with all tokens are quite high, but this is due to the bias of non-labelled tokens. Cohen's Kappa on annotated tokens only is substantial agreement with values in [0.61 -0.80] interval according to the interpretations in [START_REF] Ren | Understanding interobserver agreement: the kappa statistic[END_REF] for three pairs of raters. Computed values for both Krippendorff's alpha and Fleiss's Kappa are identical 0.70 and belong to the same previous interval, interpret as substantial agreement. Based on these computed measures and their interpretation, we can find some correlations with diagram on the Figure 2. These annotations made it possible to build a repository of annotated tokens that we combined with CamemBERT-ner results and a rule-based system to pseudo-anonymize our entire data. This combination is what we call data pseudo-anonymization chain represented by Figure 3.

Data pseudo-anonymization chain

Pseudo-anonymising sensitive information starts with recognising them, and this is done by NER task. As we use a tool based on CRF coupled with active learning to annotate our data, we test the resulting model on unseen emails and the result was very bad compared to the test performed with CamemBERT-ner 9 , a transformers based model. From the data annotation stage (carried out on the same 1k emails), there are 6219 identified strings that have been similarly annotated by the three annotators or by two of them. The difference of about 733 entities identified by CamemBERTner compared to manual annotations shows its performance in identifying elements that it cannot classify. This comes from the fact that CamemBERTner has been fine-tuned from CamemBERT on the wikiner-fr data set (∼ 170 634 sentences). We anticipate that we will have to compare these results with those obtained with fine tuning CamemBERT on our own data in order to recognise additional entities (tool, project, ID, subgroup, etc.) used by raters during the annotation process.

As can be seen in figure 4, the main challenge is to be able to reclassify on these categories those from MISC. Also the organisations recognised by CamemBERT-ner are more than double of those from the annotation and need to be reclassified into other categories like subgroup or project. A rule-based system was used to refine category of entities from MISC. During the annotation process, a repository was built, containing a list of string with their respective tags ( tags from table 1) without duplicates. The rule-based system consists of comparing strings identified as miscellaneous by CamemBERT-ner with same from the annotation repository and finally use his tag (defined by raters) to refine miscellaneous category. This Refining stage helps the rule-based system in creating semantic code for the substitution of those strings from emails content ans subjects. Created codes look like user xxxxxxxx, org xxxxxxxx, tool xxxxxxxx respectively for identified collaborators, organisations, tools and this same coding pattern was used for all other entities classes. When substituting collaborators first names or last names or both in an email, the rule-based system checks sender or recipients of the email to keep the email and conversation thread context. While running, the rulebased system builds a new repository containing all pairs of substituted string and their respective code. This repository is call correspondence table and could be use later to rebuild emails and subjects without codes but strings which was substituted before. Below is an example of a paragraph before and after pseudo-anonymisation. The above paragraph contains bold text segments that are names of collaborators and companies that have been replaced by code as in the pseudoanonymised text below.

Les espaces de coworking sont plutôt traités dans le misc 55e6a en cours de rédaction par user 8e47d, user c6f1d, user 5ff59. L'étude de user 5ff59 (seule) devait porter initialement sur la valeur pour org 252f2a des tierslieux (fablabs, espaces de co-working ) pour ce qui concerne l'apprentissage et la transmission de connaissances à distance.

A part le recentrage sur une population interne org 252f2a , l'idée générale est globalement conservée.

Everything that has been done so far has allowed to set up a data processing pipeline that takes data collected in CSV format as input and produces pseudo-anonymized data as output in the same format.

Future work

After obtaining pseudo-anonymised data, we will analyze this data in order to approach the problem of threads constitution with its inherent problems including conversation disentanglement, dialog acts identification and text segmentations. To reconstruct conversation threads, [START_REF] Domeniconi | A novel method for unsupervised and supervised conversational message thread detection[END_REF] propose an approach that combines similarity calculations of 8 features built from each email. These features actually bring out the context of an email. Taking into account the context of messages or emails allowed researchers of Yahoo and Amazon [START_REF] Avigdor-Elgrabli | More than threads: Identifying related email messages[END_REF] to automatically evaluate the semantic relationship between messages within a mailbox.

Regarding conversation disentanglement, Elsner and Charniak (2010) and [START_REF] Elsner | Disentangling chat with local coherence models[END_REF] approach chat disentanglement by first using binary classifier and local Coherence Models one year later. [START_REF] Jiang | Learning to disentangle interleaved conversational threads with a Siamese hierarchical network and similarity ranking[END_REF] take advantage of learning language representations to disentangle conversations. compute similarities between messages using a model they name Siamese Hierarchical Convolutional Neural Network (SHCNN) which is a Siamese hierarchical convolutional network.

SegBot was developed by [START_REF] Li | Segbot: A generic neural text segmentation model with pointer network[END_REF] to propose a solution to the problem of text segmentation. [START_REF] Koshorek | Text segmentation as a supervised learning task[END_REF] address text segmentation as a supervised learning problem and present a large dataset for this problem. Dialog acts identification could be seen as determining interlocutors intentions within a conversation. [START_REF] Wang | Context-aware intent identification in email conversations[END_REF] study the identification of intentions in emails in a workplace situation.

All these work give us some leads of experience to be carried out very soon with our data in order to approach our problem of threads constitution.

Conclusion

In this paper, we presented a method for building corporate corpora including different aspects and steps which allowed the creation of a pipeline to pseudo-anonymise data. Such a pipeline is a response to the constraints induced by the GDPR and the secrecy of correspondence compliance.

The process we described consists of several steps: first we prospected with our collaborators to obtain their consent agreement, this in order to be GDPR and secrecy of correspondence compliant; second we collected and pre-processed emails from Outlook mailboxes; the third step dealt with manual annotation and Named Entities Recognition; fourth, we performed data pseudo-anonymization. During the second step, we developed a tool called OutlookScraping that allowed us to collect a first batch of 11k emails on the workstations of 4 close collaborators out of 78 collaborators who gave their consent agreement.

All these steps contributed to the production of pseudo-anonymized data that we will use for our future work of thread constitution. For our constitution of threads problem, we will focus on conversations disentanglement, dialog acts identification and text segmentation.

Figure 1 .

 1 Figure 1. Number of tokens per email before and after quoted messages and signatures extraction for 2k emails sample

  to a person ROLE (B-, I-) job names (Manager, Engineer, etc.) UNCERTAIN any identified entity that does not fall into the previous tags

Figure 2 .

 2 Figure 2. Statistics of annotations by three annotators

Figure 3 .

 3 Figure 3. Data pseudo-anonymization chain

Figure 4 .

 4 Figure 4. Number of elements identified and classified by CamemBERT-ner and tags identified by annotators on 1K emails

  Les espaces de co-working sont plutôt traités dans le Research Paper en cours de rédaction par Pierre, Paul, Louise.L'étude de Louise (seule) devait porter initialement sur la valeur pour NomEntreprise des tierslieux (fablabs, espaces de coworking) pour ce qui concerne l'apprentissage et la transmission de connaissances à distance. A part le recentrage sur une population interne NomEntreprise, l'idée générale est globalement conservée.

  identify five categories of zone within emails, namely: Author Text, Signature, Advertisement(automatically appended advertising), Quoted Text (extended quotations), and Reply Lines (including forwarded and reply text). Three years later[START_REF] Vitor | Learning to extract signature and reply lines from email[END_REF] refined and extended to nine categories. Two of them caught our attention: there are Quoted Conversation Zones (reply and forwoard message) and Boilerplate Zones (signature, advertising, disclaimer and attachment).

Table 1 .

 1 Tags used to annotate 1k emails

Table 2 .

 2 Inter-annotator agreement measures on 1k emails with Cohen's Kappa, Krippendorff's alpha and Fleiss's Kappa. R i stands f or Rater i , i ∈ {1, 2, 3}; * For all tokens and # For annotated tokens only

	9 https://huggingface.co/Jean-Baptiste/
	camembert-ner

https://ec.europa.eu/info/ law/law-topic/data-protection/ data-protection-eu_en

https://docs.microsoft.com/en-us/ dotnet/desktop/wpf/overview/?view= netdesktop-5.0

https://docs.microsoft.com/en-us/ dotnet/api/microsoft.office.interop. outlook.application?view=outlook-pia

https://github.com/mailgun/talon

https://docs.python.org/3/library/re. html

https://camembert-model.fr/

https://prodi.gy/

https://inception-project.github.io/


Dima Muhammad

Jane Hubert

Nathalie Lalun

Jean-Hugues Renault

Hélène Bobichon

Mohammed Nour

Laurence Voutquenne-Nazabadioko
email: laurence.nazabadioko@univ-reims.fr

Isolation of flavonoids and triterpenoids

Keywords: Centrifugal partition extraction, Centrifugal partition chromatography, three-phase solvent system, triterpenoids, flavonoids, fruits, Alphitonia neocaledonica

come

Introduction -Alphitonia neocaledonica (Rhamnaceae) is an endemic tree of New Caledonia. Although three flavonoids were identified in the leaves, the secondary metabolite profile of the fruits has never been investigated.

Objective -Phytochemical investigation of A. neocaledonica fruits and evaluation of their antioxidant, antityrosinase and cytotoxic activities.

Methodology -A hydromethanolic extract was fractionated by liquid-liquid extraction to obtain ethyl acetate and n-butanolic fractions. The ethyl acetate-soluble part was purified by Silicagel column chromatography and high-performance liquid chromatography (HPLC). The n-butanol-soluble part was fractionated by centrifugal partition extraction (CPE) and the collected fractions were further purified by centrifugal partition chromatography (CPC) and HPLC. The chemical structures of the purified compounds were identified by nuclear magnetic resonance and mass spectrometry.

Results

-Three triterpenoids and one flavonoid were isolated from the ethyl acetate-soluble part. Fractions enriched in triterpenoids, flavonoids and catechin derivatives were obtained from the n-butanol-soluble part. Gallocatechin and flavonoids were obtained as pure compounds by further CPC and HPLC purification. The n-butanolic-soluble part showed antioxidant and antityrosinase activities due to the presence of tannins and gallocatechin. The triterpenoid alphitolic acid showed a moderate cytotoxic activity against KB cells line (IC50 = 8.5 μM). The hydromethanolic extract possess a potential cytotoxic activity due to the presence of triterpenes, and it can also be valuable as a cosmetic ingredient for its antioxidant and antityrosinase activities.

Conclusion

INTRODUCTION

New Caledonia is well known for its high biodiversity with a global endemic area of 74.3%. This high endemicity is partly due to the multiplicity of geological substrates with one-third of the total area covered by ultramafic rocks. Erosion and alteration of these rocks have led to nutrient depletion and soil enrichment with heavy metals resulting in the development of a unique flora. Alphitonia neocaledonica (Schltr.) Guillaumin (Rhamnaceae) is an endemic small forest tree of New Caledonia growing on this ultramafic soil rich in extractable nickel. This tree is found throughout the mainland, the Isle of Pines (Southern Province), Bélep and Yandé (Northern Province). The fruits are black, round, about the size of a small cherry, not edible, and contain 2-3 very hard nuclei with shiny, black and very hard seeds [START_REF] Suprin | Le (Faux) Pomaderris. Nature[END_REF]).

The fresh bark, bitter and astringent, exudes a clear balsamic odor of methyl salicylate and is traditionally used to treat skin diseases (eczema, ecthyma, pityriasis) and rheumatic pains [START_REF] Suprin | Le (Faux) Pomaderris. Nature[END_REF]. The leaves are used locally to facilitate childbirth [START_REF] Suprin | Le (Faux) Pomaderris. Nature[END_REF]. The fatty acids profile of the seeds confers a potential nutritional value to the oil [START_REF] Bombarda | Fatty acids profile of Alphitonia neocaledonica and Grevillea exul var. rubiginosa Seed Oils, Occurrence of an 5 series[END_REF].

A range of phenolic compounds have already been identified in various Alphitonia species [START_REF] Guise | Further constituents of Alphitonia species[END_REF][START_REF] Branch | Constituents of Alphitonia species. III. Alphitexolide, a new triterpene, and other extractives[END_REF][START_REF] Jou | Flavonol glycosides and cytotoxic triterpenoids from Alphitonia philippinensis[END_REF]). More precisely, three isorhamnetinderived flavonoids have been identified in the leaves of A. neocaledonica [START_REF] Lin | Flavonoids from Alphitonia neocaledonica[END_REF].

Pentacyclic triterpenoids including ceanothic acid, betulin, betulinic acid, alphitolic acid with in vitro cytotoxic, anti-inflammatory, and antimicrobial activities (Dzuback at al. 2006) have also been detected in several Alphitonia species [START_REF] Branch | Constituents of Alphitonia species. III. Alphitexolide, a new triterpene, and other extractives[END_REF][START_REF] Jou | Flavonol glycosides and cytotoxic triterpenoids from Alphitonia philippinensis[END_REF][START_REF] Setzer | Bioactive lupane triterpenoids in Alphitonia petriei from Paluma, north Queensland, Australia[END_REF].

The objective of this work was to investigate the secondary metabolite profile of a crude hydromethanolic extract of Alphitonia neocaledonica fruits, with a particular focus on flavonoids and triterpenoids which have been reported as potent antioxidant or whitening agents due to their radical scavenging [START_REF] Ko | Compounds with tyrosinase inhibition, elastase inhibition and DPPH radical scavenging activities from the branches of Distylium racemosum Sieb. et Zucc[END_REF], or tyrosinase inhibitory activities [START_REF] Parvez | Naturally occuring tyrosinase inhibitors: mechanism and applications in skin health, cosmetics and agriculture industries[END_REF][START_REF] Ullah | Tyrosinase inhibitory pentacyclic triterpenes and analgesic and spasmolytic activities of methanol extracts of Rhododendron collettianum[END_REF]. In this study, the radical scavenging ability of the extracts was investigated, as well as the tyrosinase inhibitory activity and cytotoxic activity against KB cells of some isolated compounds.

EXPERIMENTAL

Chemicals and reagents

Acetone, acetonitrile of HPLC grade, n-butanol, chloroforme, cyclohexane, dimethylsulfoxide (DMSO), ethanol, ethyl acetate, n-heptane, n-hexane, isopronalol, methanol, methyl-tert-butyl ether, methyl ethyl ketone, potassium hydroxide, pyridine, sulphuric acid, toluene, and trifluoroacetic acid (TFA) were purchased from Carlo Erba Reactifs SDS (Val de Reuil, France). Acetic acid was purchased from Acros Organics (New Jersey, USA) and formic acid was purchased from Prolabo (Paris, France). Deuterated methanol-d4, α-naphthol, DPPH, mushroom tyrosinase (EC 1.14.18.1), L-DOPA, kojic acid (purity 99%), ascorbic acid, and αhederin were purchased from Sigma-Aldrich (Saint-Quentin, France). WST-1 was obtained from Roche (Meylan, France) and DMEM F12 was purchased from Gibco-Invitrogen (Cergy Pontoise, France). The KB cell line DSMZ ACC136 was purchased from Interchim ® (Montluçon, France). Deionised water was used to prepare all aqueous solutions.

Plant material

Alphitonia neocaledonica (Schltr.) Guillaumin fruits were collected by Pr. Mohammed Nour in September 2009 at the end of the cool season in southern province. The botanical identification was made at the Laboratoire Insulaire du Vivant et de l'Environnement of the New Caledonia University. A voucher specimen (09NM001) was deposited in the Herbarium of Noumea (New Caledonia).

Apparatus

Centrifugal partition extraction.

A laboratory-scale Fast Centrifugal Partition Extractor FCPE300 ® (Kromaton Technology, Angers, France) of 303.5 mL capacity and containing a total of 231 partition twin-cells (~1 mL each) was used to fractionate the n-butanol extract of A. neocaledonica fruits. The mobile phase was pumped by a Preparative Pump 1800W V7115 (Knauer, Berlin, Germany). The system was coupled to a UVD 170S detector (Dionex, Sunnivale, CA, USA) monitored at 210 and 254 nm. Fractions of 20 mL were collected by a Pharmacia Superfrac collector (Uppsala, Sweden). All experiments were carried out at room temperature (22 ± 2 °C).

Centrifugal partition chromatography.

CPC was performed on an ASCPC250 ® apparatus (Armen Instrument, Vannes, France) of 250 mL capacity and containing 1890 partition twin-cells (~0.1 mL). The solvents were pumped by a binary gradient Armen AP-50 pump. The system was coupled to an AR-600 UV/Vis detector (Armen) monitored at 210 and 254 nm.

Fractions of 4 mL were collected by an Armen LS-5600 collector. Data were acquired using the Armen Glider CPC control software version 2.9. Experiments were conducted at room temperature (22 ± 2 °C).

Preparative HPLC. Preparative HPLC was performed on a NW 50 preparative HPLC system equipped with a Merck column of 180 x 50 mm packed with a Lichroprep C18 reversed phase (40-63 µm) (Merck, Darmstadt, Germany). The mobile phase was pumped by an Armen quaternary gradient pump AP 250/500 pump and the sample were introduced into the HPLC column via an Armen ACC 250/500 injector equipped with a 10 mL sample loop. The effluent was monitored at 254 nm by a Knauer K-2501 UV/Vis detector and plotted with a Kipp & Zonen recorder (Emerainville, France). The fractions were collected with a Büchi C-660 collector (Rungis, France).

Analytical and semi-preparative HPLC. Analytical and semi-preparative HPLC were performed on a Dionex apparatus equipped with an ASI-100 automated sample injector, a STH 585 column oven, a P580 pump, a diode array detector UVD 340S and the Chromeleon ® software version 6.8. Semi-preparative HPLC separations were performed on a prepacked C18 reversed phase column Luna (250 x 10mm, 5m, Phenomenex, France). Analytical HPLC separations were performed on a prepacked C18 reversed phase column Luna (250 x 4.6 mm, 5m, Phenomenex, France). The chromatograms were monitored at 205, 210, 254 and 312 nm.

Open column liquid chromatography (CC) and thin layer chromatography (TLC). CC was

performed over Kieselgel 60 Merck,Darmstadt,Germany). Preparative and analytical TLC were carried out on precoated silica gel 60 F254 plates (Merck, Darmstadt, Germany). Spots were visualized after spraying with 50% sulfuric acid and heating at 100 °C for 1 min.

Optical rotation. Optical rotations were measured in methanol or chloroform with a Perkin-Elmer 241 polarimeter (Perkin Elmer, Courtaboeuf, France).

NMR analyses. 1 H and 13 C NMR spectra were recorded on a Bruker Avance III 500 spectrometer ( 1 H at 500 MHz and 13 C at 125 MHz, Brucker, Wissembourg, France) or on a Bruker Avance III 600 spectrometer equipped with a cryoprobe ( 1 H at 600 MHz and 13 C at 150 MHz). 2D-NMR experiments were performed using standard Bruker microprograms.

Chemical shift (δ) were reported in ppm using the internal solvent resonances at H 3.33 and C 47.6 (CD3OD).

ESI-MS analyses. High resolution ESI-MS

Preparation of crude extracts

Powdered air-dried fruits of A. neocaledonica (475 g) were macerated in 5 L methanol:water (8:2, v/v) over night and then lixiviated with 15 L methanol:water (8:2, v/v). After methanol evaporation under reduced pressure, the resulting aqueous solution (1 L) was extracted successively with cyclohexane, ethyl acetate and n-butanol (3 x 1 L each), then dried under reduced pressure to yield a cyclohexane-soluble part of 0.75 g, a ethyl acetate-soluble part of 3.6 g, a n-butanol-soluble part of 41.2 g and a water-soluble part of 140.3 g (Fig. 1).

Separation procedure of the ethyl acetate-soluble part

The ethyl acetate-soluble part (3.6 g) was purified by Silicagel CC using a gradient of cyclohexane:chloroform:methanol (3:7:0 to 0:0:1, v/v), to afford 60 fractions (200 mL each).

Fractions [8-9] (443.8 mg) and [42-44] (859.3 mg) were further purified by Silicagel CC and subsequently by preparative TLC (Fig. 1). Fractions [57-58] (597.2 mg) were purified by preparative HPLC using a binary gradient of methanol:water (2:8 to 1:0, v/v) in 210 min at 35 mL/min to yield 120 fractions. Fractions [111][112][113] (29.1 mg) were further purified by semipreparative HPLC by using a gradient of acetonitrile:water (3:7 to 6:4, v/v) (see Supporting information for more details).

CPE fractionation of the n-butanol-soluble part

The solvent system was prepared by mixing n-heptane (500 mL), methyl-tert-butyl-ether (500 mL), acetonitrile (500 mL) and water (500 mL) in a separatory funnel. After separation of the n-heptane-rich upper phase (UP), one equivalent volume of methyl-tert-butyl-ether (500 mL) was added to the mixture of middle and lower phases in order to slightly reduce the polarity of the remaining two-phase solvent system [START_REF] Hamzaoui | Stepwise elution of a three-phase solvent system in centrifugal partition extraction: A new strategy for the fractionation and phytochemical screening of a crude bark extract[END_REF]. After decantation, the final middle and lower phases were separated. The fractionation was performed at 20 mL/min with a column rotation speed of 1000 rpm. Four g of the n-butanol-soluble part of A. neocaledonica fruits were solubilized in 30 mL of an LP/UP mixture (8:2, v/v) and loaded into the column. The upper liquid phase was pumped for 27 min to elute all hydrophobic compounds. The moderately polar middle phase was then pumped for 30 min to elute the moderately polar compounds, and finally the aqueous lower phase was pumped in the descending mode for 15 min in order to recover the most hydrophilic compounds retained inside the column.

All fractions were checked by TLC developed with toluene:acetone:formic acid (6:6:1, v/v) and those showing the same profile were pooled to yield eight sub-fractions noted from F1 to F8 [F1: 597 mg, F2: 35.6 mg, F3: 137.8 mg, F4: 402.6 mg, F5: 92.6 mg, F6: 293.0 mg, F7: 120.5 mg, F8: 1590.3 mg]. These sub-fractions were analyzed by analytical HPLC using a gradient of acetonitrile:0.025% TFA in water (1:9 to 4:6, v/v) for 35 min.

Purification of the CPE-fractions F3 to F6

Fractions F3, a part of fraction F4 (90.0 mg), fraction F5 and a part of fraction F6 (90.0 mg) were purified by semi-preparative HPLC (Fig. 1) by using a gradient of acetonitrile:0.025% TFA in water (see Supporting information for more details).

A part of fraction F4 (290 mg) was solubilized in 20 mL of aqueous phase/organic phase (8:2, v/v) of the biphasic solvent system n-hexane:ethyl acetate:methanol:water (1:9:1:9, v/v). The CPC column was filled by the aqueous stationary phase. The organic mobile phase was pumped at 4 mL/min with a rotation speed of 1200 rpm in the ascending mode in order to equilibrate the CPC column. The sample was then injected and the organic mobile phase was pumped at 4 mL/min for 180 min. Finally, the aqueous phase was pumped in the descending mode at 20 mL/min for 50 min. The CPC-fractions [186][187][188][189][190][191][192] (27.5 mg) were purified by semi-preparative HPLC using a binary gradient of acetonitrile:0.025% TFA in water (1:9, v/v) for 10 min, from 1:9 to 23:77 (v/v) in 3 min, and to 25:75 (v/v) in 23 min at 4 mL/min.

Acid hydrolysis and identification of sugar units

One g of the crude methanol:water extract was refluxed with 25 mL of 2N trifluoroacetic acid for 4 h. After extraction with ethyl acetate (3 × 25 mL), the aqueous layer was neutralized to pH 6 with 50 mM potassium hydroxide and freeze-dried to provide the monosaccharide residue.

The sugars profile was determined by comparison with authentic samples on TLC in methyl ethyl ketone:iso-propanol:acetone:water (20:10:7:6, v/v). Detection was performed with αnaphthol. The monosaccharide residue (40 mg) was solubilized in water (1 mL) and purified by semi-preparative HPLC, on a specific column ROA (250 x 15 mm, T = 35 °C) eluted isocratically with a solution of 0.25 µM sulphuric acid at a flow rate of 3.5 mL/min, to give the four sugars. The chromatogram was monitored by a refractive index detector RI-410 (T = 35 °C). Each fraction was neutralized with a 50 mM solution of potassium hydroxide, dried, dissolved in 1 mL pyridine, and filtered to remove potassium sulfate salts. After pyridine evaporation, the sugars were dissolved (0.1-0.5 mg/mL) in a mixture nhexane:ethanol:trifluoroacetic acid (70:30:1, v/v) and analyzed by HPLC on an analytical chiral column Chiralpak ® ICA, using the mobile phase n-hexane:ethanol:trifluoroacetic acid (80:20:0.1, v/v) isocratically at a flow rate of 0.5 mL/min. Chromatograms were monitored by a refractive index detector RI-410 and identification of the sugars was carried out by comparing the retention times of standard sugars (Table 1).

Structure identification of compounds

WST cytotoxicity assay

The cytotoxic activities of compounds 1-3 on KB cell lines (ATTC ® CCL TM -17) were determined by colorimetry based on the cleavage of the WST-1 tetrazolium salt. Tetrazolium salts were cleaved to formazan dye by cellular enzymes in the viable cells, and the level of absorbance was directly correlated to the metabolically active cells. The stock solutions of triterpenoids (1 mg/mL) were prepared in dimethylsulfoxide. Sample dilutions were then performed in medium DMEM F12 (1, 2.5, 5, 7.5 or 10 μg/mL). The cells were prepared as previously published, using DMEM F12 medium [START_REF] Chwalek | Structureactivity relationships of some hederagenin diglycosides: haemolysis, cytotoxicity and apoptosis induction[END_REF]. After removal of preincubated culture medium, 200 μL of DMEM F12 containing various concentrations of samples were added and further incubated for 48 h at 37 °C. Cell viability was determined by adding WST-1 tetrazolium salt and by measuring the absorbance at 450 nm after ≈1 h. Each assay was realized in triplicate in 96-well microplates. A dose-response curve was plotted for each compound, and the concentration giving 50% inhibition (IC50) was calculated by using MSExcel based program. -hederin was employed as a positive control, which exhibited an IC50 value of 5.5 μM under the above conditions [START_REF] Chwalek | Structureactivity relationships of some hederagenin diglycosides: haemolysis, cytotoxicity and apoptosis induction[END_REF].

DPPH radical scavenging assay

The free radical scavenging capacity of the ethyl acetate-and n-butanol-soluble parts of A. neocaledonica fruits was determined by using the stable 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical [START_REF] Cheng | High-throughput relative DPPH radical scavenging capacity assay[END_REF]. Briefly, a stock solution of DPPH was prepared at 158 µM in ethanol:water (1:1, v/v). Each sample was dissolved in dimethylsulfoxide (200 µg/mL) and 5 µL were added to the DPPH stock solution (95 µL), in triplicate in 96-well plates. The DPPH • absorbance in each reaction mixture was monitored at 515 nm.

Absorbance measurements were performed at regular interval of 1.5 min for 30 min at 37 °C for all samples. The n-butanol-soluble part was then tested at 200, 100, 50 and 10 µg/mL to calculate the concentration able to quench 50% of the reaction system (EC50) at 30 min. The ethanol:water (1:1, v/v) solution was used as a blank, the free DPPH solution was used as a negative control and ascorbic acid (5 µg/mL) was used as a positive control. Results were expressed as percentage decrease with respect to control values.

Tyrosinase inhibitory activity assay

The tyrosinase inhibitory activity of the n-butanol-soluble part, of the CPE-fractions F1, F3-F6 and of the isolated pure compounds 1, 2, 5-10 was determined against mushroom tyrosinase.

The assay was performed according to a slight modification of a previously described method using L-DOPA as substrate (Alabdul [START_REF] Magid | Tyrosinase inhibitors and sesquiterpene diglycosides from Guioa villosa[END_REF]. Briefly, the tested compounds were dissolved in 10% DMSO and mixed (1:1) with sodium-phosphate buffer (PBS, pH 6.8) to obtain a concentration of 4 mg/mL for the extracts or 1 mg/mL for the compounds. Tyrosinase (100 µL; 135 U/mL) was first pre-incubated with the tested compounds (100 µL) at 25 °C for 10 min, and then 100 µL of L-DOPA (0.5 mM, PBS pH 6.8) was added. The

RESULTS AND DISCUSSION

The presence of triterpenes, polyphenols, and a high amount of condensed tannins firstly highlighted by screening the hydromethanolic extract of A. neocaledonica fruits by analytical TLC and HPLC. The hydromethanolic extract was preliminarily partitioned by liquid-liquid extraction to give an ethyl acetate-and a n-butanol-soluble parts. Extraction yield, antioxidant activity and anti-tyrosinase activity of these ethyl acetate-and a n-butanol-soluble-parts are given in Table 2. The n-butanol soluble part was the most active with an EC50 = 20 µg/mL for the DPPH radical scavenging activity and 92.1% inhibition of the tyrosinase activity. The nbutanol-soluble part was thus investigated for identification of phenolic compounds. Although less active as compared to the n-butanol-soluble part, the secondary metabolite profile of the ethyl acetate-soluble part was also investigated in order to screen A. neocaledonica fruits in the broadest possible sense.

Characterization of the ethyl acetate -soluble part

The ethyl acetate-soluble part was fractionated by silicagel CC. Compound 1 (141.3 mg) was detected in fractions [22][23][24][25][26][27][28][29][30][31][32][33][34][35], and 2 was detected in fractions [41] (325.3 mg) and [22][23][24][25][26][27] (134.9 mg). Compounds 2 (8.9 mg) and 3 (6.5 mg) were also obtained by preparative TLC of fractions [62-65] (Fig. 1). These compounds were identified as three known triterpenes including two major products derived from lupane: betulinic acid (1) [START_REF] Ikuta | Triterpenoids of Paeonia japonica callus tissue[END_REF]Itokawa 1988, Jou et al. 2004), and alphitolic acid (2) [START_REF] Aguirre | Topical anti-inflammatory activity of 2α-hydroxy pentacyclic triterpene acids from the leaves of Ugni molinae[END_REF], as well as an ursane derivative: corosolic acid (3) [START_REF] Aguirre | Topical anti-inflammatory activity of 2α-hydroxy pentacyclic triterpene acids from the leaves of Ugni molinae[END_REF]) (Fig. 2). The known flavonoid 3-O-[6"-E-feruloyl]-β-D-glucopyranosylisorhamnetin (4) [START_REF] Wang | Two new flavonol glycosides from Sarcopyramis bodinieri var[END_REF]) (Fig. 2) was also purified by combination of preparative and semi-preparative HPLC of fractions [57-58] (Rt = 9.8, 3.4 mg). Even if minor, this flavonoid contributed probably to the slight but significant DPPH radical scavenging activity of the ethyl acetate-soluble part (Table 2). The slight tyrosinase inhibitory activity (10%) of the ethyl acetate-soluble part (Table 2) might be attributed to the presence of betulinic acid (1) and alphitolic acid (2) which display 45.5 and 36.5 % inhibition, respectively (Table 3). The very small recovered quantity of corosolic acid (3) did not enable the evaluation of its tyrosinase inhibitory activity.

WST cytotoxic activity of triterpenes

The cytotoxic activity of betulinic acid (1) has been previously reported against more than 40 cell lines derived from the most common human cancers (breast, colon, brain, lung and prostate) with an IC50 ranging from 1.0 to 11.6 µg/mL by MTT assay, with a good selectivity on cancer cell lines with respect to healthy human cells, a low toxicity and a wide therapeutic index (Dzuback et al., 2006, Shanmugan et al. 2012). It has been also tested in clinical phase II trials (Dzuback et al., 2006). Alphitolic acid (2) [START_REF] Kim | Cytotoxic triterpenoids from Berberis koreana[END_REF]) and corosolic acid (3) [START_REF] Salvador | Ursane-type pentacyclic triterpenoids as useful platforme to discover anticancer drugs[END_REF]) have also shown a cytotoxic activity against various cell lines but not KB cells for corosolic acid. In order to compare the cytotoxic activities of these compounds (1-3) against KB cell line, using the same method, a WST-1 proliferation test, we determined their IC50 values after 48 h of contact of the compounds with the cells. Betulinic acid (1) and corosolic acid (3) showed a low cytotoxic activity with inhibitions of 45.7% and 49.3%, respectively at 10 µg/mL (IC50 > 10 μg/mL) and alphitolic acid (2) showed a moderate cytotoxic activity with an IC50 of 4 ± 0.3 μg/mL (8.5 μM). This is the first comparison of these compounds against KB cells line.

Characterisation of the n-butanol-soluble part

Centrifugal Partition Extraction was used to prefractionate the n-butanol-soluble part and to separate the abundant mixture of tannins from the other potentially interesting constituents. This solid support-free liquid-liquid fractionation technique was particularly suited for this purpose due to the liquid nature of the stationary phases which enables a total recovery of the injected mass while avoiding irreversible adsorption of polar compounds as observed on polymeric resins or silica gel [START_REF] Hamzaoui | Stepwise elution of a three-phase solvent system in centrifugal partition extraction: A new strategy for the fractionation and phytochemical screening of a crude bark extract[END_REF]). As a result, eight fractions of simplified chemical composition were obtained from 4 g of the n-butanol-soluble part, with the last fractions F7 and F8 containing all the tannins (75.7%) retained inside the column over the whole separation process. According to UV spectra obtained from HPLC-DAD chromatogram and TLC analyses, fractions F1-F2, eluted with the n-heptane-rich upper phase, comprised a diversity of triterpenoids, while fractions F3-F6, eluted with the methyl-tert-butyl ether/acetonitrile-rich intermediate phase, mostly contained flavonoids and phenolic compounds.

CPC and semi-preparative HPLC were then used as orthogonal techniques to further purify fractions F3 to F6 (Fig. 1). Fraction F3 resulted in the purification of compounds 6 (Rt = 28.2, 4.4 mg), 5 (Rt = 30.2, 6 mg), 9 (Rt = 33.7, 2.6 mg) and 8 (Rt = 34.3, 2.0 mg). Fraction F4 yielded compounds 6 again (Rt = 29.3, 5.6 mg) and 5 (Rt = 33.5, 8.1 mg). Fraction F5 give compounds 7 (Rt = 15.5, 2.4 mg) and 9 (Rt = 21.4, 3.1 mg) and 8 (Rt = 11.1, 2.2 mg), 7 (Rt = 15.2, 3.1 mg) and 9 (Rt = 21.1, 2.8 mg) were isolated from fraction F6 (Fig. 1). During the purification of fraction F4, compounds 5 and 6 were eluted separately but 10 was eluted with a mixture of derivatives. Fraction F4 was thus purify by CPC and compounds 5 (15 mg), 6 (5.0 mg) and 10 (10.9 mg) were obtained (Fig. 1).

Compounds 5-8 were identified as four known flavonoids 3-O-β-D-galactopyranosylisorhamnetin (5) [START_REF] Bierner | Flavonol 3-glycosides in Amblyolepis setigera[END_REF]), 3-O-β-D-galactopyranosyl-quercetin (6) [START_REF] Bierner | Flavonol 3-glycosides in Amblyolepis setigera[END_REF] [START_REF] Buschi | Isorhamnetin 3-O-robinobioside from Gomphrena martiana[END_REF], and 3-O-[β-D-glucopyranosyl-(1→2) -α-L-rhamnopyranosyl]-quercetin (8) [START_REF] Hasler | Complex flavonol glycosides from the leaves of Ginkgo biloba[END_REF]) (Fig. 2). Compound 10 was identified as (+) gallocatechin [START_REF] Foo | Proanthocyanidins from Lotus pedunculatus[END_REF].

, 3-O- [α-L-rhamnopyranosyl-(1→6)-β-D-galactopyranosyl]-isorhamnetin (7)
Compound 9 was a yellow powder with an [α]D of -44.0 (c 0.1, methanol). The molecular formula C42H46O23 was obtained from the HR-ESI-MS analysis in the positive ionization mode.

A pseudo-molecular ion at m/z 941.2324 [M + Na] + (calcd 941.2328) was also detected. 1 H NMR signals of quercetin were observed at δH 6.14 (s,2H),7.36 (d,J= 2.2 Hz,6.92 (d,J= 8.4 Hz,J= 8.4,2.2 Hz, as for 6 and 8. The saccharide part was characterized by the presence of three anomeric protons detected at δH 4.50 (d,J= 7.7 Hz),4.53 (d,J= 8.1 Hz) and 4.72 ppm (d, J= 2.0 Hz) with their corresponding anomeric carbons at δC 106.2, 104.0 and 98.9, respectively (Fig. S1 and S2 in supporting information), attributed after analysis of the HSQC-Jmodulate spectrum. Combined analysis of the COSY, HSQC-Jmodulate spectra and coupling constants from the 1 H NMR spectrum allowed us to identify three sugars, a α-L-rhamnopyranose substituted in position 2 (δC 82.6), a β-D-glucopyranose esterified in position 6 with its deshielded signals at δH 4.16 (dd,J= 11.8,6.0 Hz,J= 11.8,2.9 Hz, H-6b"') and a terminal β-D-xylopyranose (Table 4). D-Glucose, D-galactose, Lrhamnose, and D-xylose were identified after acid hydrolysis of the hydromethanolic extract (Table 1). To confirm the presence of L-rhamnose in our samples the optical rotation of the isolated L-rhamnose was measured. 1 H NMR spectrum also revealed signals of a methoxy group (δH 3.79), a second ABX system at δH 6.93 (d, J= 1.9 Hz, H-2 "''), δH 6.69 (d, J= 8.2 Hz, H-5"''') and δH 6.87 (dd, J= 8.2, 1.9 Hz, H-6"'''), indicating the presence of a second trisubstituted aromatic ring, and signals of two olefinic doublets at δH 7.45 and 6.13 (d, J= 15.9 Hz), suggesting a substitution by an organic acid derivative of cinnamic acid. These two olefinic protons were correlated in the HMBC spectrum with the signal of a carbonyl group at δC 167.4, indicating the presence of an α,β-unsaturated ketone. Analysis of the NOESY spectrum showed a correlation between the methoxy protons (δH 3.79) and proton H-2'''''' (δH 6.93, d, J=1.9 Hz), and allowed us to locate the methoxy group at C-3''''. These data resulted in the identification of the organic acid as ferulic acid as in compound 5 [START_REF] Wang | Two new flavonol glycosides from Sarcopyramis bodinieri var[END_REF]. The sequence Xyl-(1→2)-Glc-(1→2)-Rha of the three sugars was established from the HMBC correlations observed between H-1"' Glc/C-2'' Rha and H-1"" Xyl/C-2"' Glc and inversely H-2" Rha/Glc C-1"' and H-2"' Glc/C-1"" Xyl. These results are in agreement with those obtained by analysis of the NOESY spectrum showing correlations between H-2" Rha/H-1"' Glc and H-2"' Glc/H-1"" Xyl. The quercetin was glycosylated at position 3 as observed by the HMBC correlations between H-1"Rha/C-3 and the esterification of the glucose unit was confirmed by the two HMBC correlations observed between the carbonyl of ferulic acid and both geminal protons Ha, b-6 "' Glc. On the basis of these data, the structure of compound 9 was established as 3-O- All flavonoids (4-9) of the fruits were detected for the first time in Alphitonia species and were different from the flavonoids previously identified in the leaves [START_REF] Lin | Flavonoids from Alphitonia neocaledonica[END_REF].

Flavonoids 5, 6, and 8 have already demonstrated a DPPH radical scavenging activity with an IC50 = 67.92 µM, 4.29 µM [START_REF] Demirkiran | Antioxydant and tyrosinase inhibitory activities of flavonoids from Trifolium nigrescens subsp. petrisavi[END_REF]) and 26.4 µM [START_REF] Tang | Coumaroyl flavonol glycosides from the leaves of Gingko biloba[END_REF], respectively. Similarly, the DPPH radical scavenging activity of gallocatechin 10 has already been demonstrated with an IC50 = 6.7 µg/mL [START_REF] Ko | Compounds with tyrosinase inhibition, elastase inhibition and DPPH radical scavenging activities from the branches of Distylium racemosum Sieb. et Zucc[END_REF]). These compounds are thus probably significantly involved in the DPPH radical scavenging activity of the n-butanolsoluble part (EC50 = 20 µg/mL). This extract was however less active than the positive control ascorbic acid (EC50 = 5 µg/mL).

Tyrosinase inhibitory activity of the n-butanol-soluble part

The CPE-fractions F1, F3-F6 (Table 3) and the isolated pure compounds 5-10 (Fig. 2) were evaluated for their anti-tyrosinase activity. Fraction F1 with triterpenes showed a slight inhibitory activity (25.6%). Fractions F3 and F4 with a similar profile (flavonoids and catechins derivatives), exhibited similar inhibition activities (67.2 and 68.0%), and both fractions F5 and F6 rich in flavonoids and a part of tannins had both a significant activity (73.4 and 58.3%). All compounds (5-10) isolated from fractions F3 to F6 were tested at 1 mg/mL and no interesting activity was observed with the five flavonoids (5-9) (Table 3), probably due to the glycosylation at C-3, as observed previously with various flavonoids [START_REF] Parvez | Naturally occuring tyrosinase inhibitors: mechanism and applications in skin health, cosmetics and agriculture industries[END_REF]. Gallocatechin 10 inhibits 87.4% of the enzymatic activity, this activity has already been demonstrated with an IC50 = 4.8 µg/mL [START_REF] Ko | Compounds with tyrosinase inhibition, elastase inhibition and DPPH radical scavenging activities from the branches of Distylium racemosum Sieb. et Zucc[END_REF]. Thus, the high antityrosinase activity of the n-butanol-soluble part could be explained by the presence of tannins and/or catechine derivatives. This activity may prove to have practical values as future cosmetic ingredients.

In conclusion, Alphitonia neocaledonica fruits are readily available because this species is used in revegetation of New-Caledonian serpentine nickel-mining soils. These fruits can be valuable, as topic utilization, for their antioxidant and antityrosinase activities due to their phenolic compounds. However attention must be given because these fruits possess two abundant triterpenes, betulinic acid and alphitolic acid, with cytotoxic activity against various cell lines. For this reason a cytotoxic evaluation of the fruits could be made previous utilization. It should also be noted that these fruits could also be used as a new source of betulinic acid.

  Isolation of Flavonoids and Triterpenoids from the Fruits of Alphitonia Neocaledonica and Evaluation of their Anti-oxidant, Anti-tyrosinase and Cytotoxic Activities Dima Muhammad, Jane Hubert, Nathalie Lalun, Jean-Hugues Renault, Hélène Bobichon, Mohammed Nour, Laurence Voutquenne-Nazabadioko To cite this version: Dima Muhammad, Jane Hubert, Nathalie Lalun, Jean-Hugues Renault, Hélène Bobichon, et al.. Isolation of Flavonoids and Triterpenoids from the Fruits of Alphitonia Neocaledonica and Evaluation of their Anti-oxidant, Anti-tyrosinase and Cytotoxic Activities. Phytochemical Analysis, 2015, 26 (2), pp.137-144. 10.1002/pca.2545. hal-01994982

  -Nine known compounds including three triterpenes, five flavonoids and (+) gallocatechin, as well as a new 3-O-(6-E-feruloyl)-β-D-glucopyranosyl-(1→2)-[β-Dxylopyranosyl-(1→2)-]α-L-rhamnopyranosyl-quercetin, were isolated from A. neocaledonia fruits.

  experiments were performed using a hybrid quadrupole/time-of-flight (Q-TOF) instrument, equipped with a pneumatically assisted electrospray ion source operated in positive ionization mode (Micromass, Manchester, UK). The samples were introduced by direct infusion in a solution of methanol at a flow rate of 5 µL/min. The spray capillary voltage was set at 3500V, and the extraction cone voltage between 30-60V. The source temperature was 80 °C and the desolvation temperature was 100 °C. Biological activity measurements. 96-well microplates Greiner ® F Bottom (BMG-LABTECH, Champigny sur Marne, France) and a BMG-LABTECH UV-Vis Spectrophotometer Micro-plate reader FLUOstar Omega were used for absorbance measurements in biological assays.

  All compounds were identified by extensive spectroscopic methods including 1D-( 1 H and 13 C) and 2D-NMR (COSY, HSQC, HMBC and NOESY) experiments as well as HR-ESI-MS analysis and by comparison with spectral data from the literature values for the known compounds.

  enzyme reaction was monitored by measuring the change in absorbance at 475 nm (at 25 °C) after 10 min incubation. These solutions were prepared in triplicate in 96-well plates. Kojic acid (1 mM) was used as positive control. The inhibitory percentage of tyrosinase was calculated as follows: % inhibition = {[(A -B) -(C -D)]/(A -B)} × 100 (A: OD at 475 nm without test substance; B: OD at 475 nm without test substance and tyrosinase; C: OD at 475 nm with test substance; D: OD at 475 nm with test substance, but without tyrosinase).

  (6-E-feruloyl)-β-D-glucopyranosyl-(1→2)-[β-D-xylopyranosyl-(1→2)-]α-L-rhamnopyranosylquercetin (Fig. 3).

Figure 2 :Figure

 2 Figure 2: Structures of the known compounds (1-8, 10) isolated from A. neocaledonica fruits

Table 1 :

 1 Time retention of sugars

	Sucre	tr (min)
	D-glucose (α & β)	18.3-23.2
	D-galactose (α & β)	24.8-26.8
	L-rhamnose (α & β)	11.7
	L-arabinose (α & β)	14.6-15.5
	D-xylose (α & β)	16.6-18.5

Table 2 :

 2 Extraction yield, antioxidant and anti-tyrosinase activities of A. neocaledonica fruits

		extraction yield	antioxidant activity	Tyrosinase inhibitory activity
		(%)	(%) at 200 µg/mL	(%) at 4 mg/mL
	Ethyl acetate	0.76	44.7	10
	n-Butanol	8.76	93.2	92.1
	Water	29.54	nt	nt
	Ascorbic acid		100
	Kojic acid (1mM)			69.3 ± 1.6
	nt : not tested

Table 3 :

 3 Effect of CPE fractions and compounds 1, 2, 5-10 on anti-tyrosinase activity

		Percent inhibition at	Percent inhibition at
		4 mg/mL (mean ± SD)	1mg/mL (mean ± SD)
	F1	25.6 ± 0.7
	F3	67.2 ± 1.6
	F4	68.0 ± 1.7
	F5	73.4 ± 1.5
	F6	58.3 ± 0.2
	1		45.5 ± 1.2
	2		36.5 ± 0.6
	5		6.4 ± 1.6
	6		2.5 ± 1.7
	7		14.0 ± 0.3
	8		9.2 ± 1.3
	9		14.4 ± 1.2
	10		87.4 ± 2.2

Table 4 :

 4 1 H (600 MHz) and 13 C (150 MHz) NMR data of compound 9.Figure 1. Purification of the fruits of Alphitonia neocaledonica

	Position δC	δH (J in Hz)	Position	δC	δH (J in Hz)
	-			Glc
	2	156.7 -	1"'	104.0 4.53, d (8.1)
	3	135.2 -	2"'	83.7	3.42, dd (9.2, 8.1)
	4	178.0 -	3"'	76.0	3.62, t (9.2)
	5	161.6 -	4"'	70.2	3.38, t (9.5)
	6	98.9	6.14, sl	5"'	73.9	3.47, ddd (9.7, 6.0, 2.9)
	7	165.9 -	6"'	62.7	4.16, dd (11.8, 6.0)
	8

Acknowledgments

The authors thank the 'Région Champagne-Ardenne' and the 'Département de la Marne' for financial support. The EU-programme FEDER to the PlAneT CPER project is also gratefully acknowledged. Dominique HARAKAT (ICMR, UMR 7312) was also thanks to registry the MS spectra.

Supporting information

Supporting information can be found in the online version of this article.