Guillaume Duboisdindien
email: guillaume.duboisdindien@univ-lille.fr

Georgette Dal
email: georgette.dal@univ-lille.fr

Critical analysis of clinical resources and tools for derivational morphology used in francophone speech and language therapy

An increasing number of speech and language pathologists (SLP) tools are being marketed by specialized French-language publishers. Given the clinical focus of these tools, a critical approach to their evaluation is required. The aim of this preliminary study is to identify the main characteristics of the clinical resources designed for derivational morphology used by French-speaking SLPs. Method: A french criterionreferenced and critical analysis grid was developed to collect and analyze data from 15 resources for morphological remediation and/or learning. Results: The corpus of occurrences compiled from the 15 clinical tools is a collection of 8251 entries. The collected structures were automatically filtered and revealed 5134 occurrences of (presumed) complex lexemes. We present in this paper the 10 most frequent lexemes in such tools. The preliminary results of this study indicate that the francophone remedial materials used by SLPs for working on morphology and derivational morphology present weaknesses in their general characteristics, in the typology of the morphological tasks provided, and in the efficacy of the choice of derivational lexemes targeted for remedial treatment.

Introduction & Context

Over the past fifteen years, several studies have collected developmental and clinical data on the developmental role played by derivational morphology in word reading, literacy and vocabulary [START_REF] Carlisle | Morphological awareness and early reading achievement[END_REF][START_REF] Nation | Beyond phonological skills: broader language skills contribute to the development of reading[END_REF][START_REF] Reed | A Synthesis of Morphology Interventions and Effects on Reading Outcomes for Students in Grades K-12[END_REF]. In adults, studies suggest that the mechanisms devoted to word formation can be altered and generate combinations that deviate from the derivational rules in the case of neurological disorders such as post-stroke aphasia or the semantic variant of Primary Progressive Aphasia [START_REF] Badecker | Morphology and aphasia[END_REF][START_REF] Auclair-Ouellet | Conception or *conceivation? The processing of derivational morphology in semantic dementia[END_REF]. Some promising avenues of clinical research in children, with more modest outcomes in adults, explore treatment models that include derivational morphology among their active components [START_REF] Goodwin | A meta-analysis of morphological interventions: Effects on literacy achievement of children with literacy difficulties[END_REF][START_REF] Galuschka | The Diagnosis and Treatment of Reading and/or Spelling Disorders in Children and Adolescents[END_REF]. Alongside these studies, increasing numbers of Speech and Language Pathology (SLP) and educational tools are being marketed by specialized francophone publishers. However, their efficacy remains to be proven. Given the clinical focus of these tools, a critical approach to their evaluation is needed [START_REF] Profetto-Mcgrath | Critical Thinking and Evidence-based Practice[END_REF][START_REF] Gregory | Science-based practice and the speech-language pathologist[END_REF].

Empirical evaluations of the efficacy of remediation tools and resources is rather tricky to find. For several years, studies in the theory of decision-making behaviour [START_REF] Bettman | Consumer decision making[END_REF][START_REF] Alba | Dimensions of consumer expertise[END_REF] have shown that the marketing community -including the publishing industry -has consistently used the influence of cognitive bias to guide users' choices. As an example, some studies show that the adaptations (e.g. modifying the text font, creating contrast through use of a coloured background, adjusting line spacing) that publishers design for children's literature for learners, people with dyslexia and/or poor readers1 , are not equally or even demonstrably beneficial [START_REF] Bachmann | Dyslexia and Fonts: Is a Specific Font Useful?[END_REF][START_REF] Hakvoort | Improvements in reading accuracy as a result of increased interletter spacing are not specific to children with dyslexia[END_REF][START_REF] Sanne | Dyslexie font does not benefit reading in children with or without dyslexia[END_REF]. Thanks to cognitive bias, it is possible to play on a certain number of beliefs about these adaptations in order to convince the public of their efficacy as therapeutic or educational tools. Critical thinking is a good springboard for questioning user behaviour and tendencies regarding their choice of tools or practices [START_REF] Law | Mapping practice onto theory: the speech and language practitioner's construction of receptive language impairment[END_REF].

In order to determine the uses and needs of Speech and Language Pathologists (SLPs) with respect to derivational morphology, a survey was conducted via the French Demonext Project2 [START_REF] Namer | ParaDis and Démonette, From Theory to Resources for Derivational Paradigms[END_REF] starting in November 2020. It was addressed to SLPs in a number of French-speaking areas (France, Belgium, Switzerland, Canada, Monaco, Luxembourg, Niger). The initial results of this survey, which is still in progress, indicate that 35% of SLPs design treatment targeting derivational morphology. Ten percent of them estimate their level of knowledge on this topic at very little to little awareness and 23% claim to have general awareness of derivational morphology. However, the qualitative analysis of the corpora of spontaneous responses regarding the types of activities they propose and their terminological and theoretical knowledge of derivational morphology suggests that the gaps are 10 to 20% greater than the gaps revealed by the self-assessment among 387 respondents.

The choice of clinical and learning materials specific to derivational morphology is thus ripe for a deeper investigation. The three research questions developed for this preliminary study are:

1) What are the qualitative characteristics of French speech and language materials for clinical activities in morphology? 2) Are there any preferred (presumed) complex lexemes found in therapeutic tools targeting derivational morphology? If so, which ones and what are their properties? 3) What tasks are used most frequently to stimulate the mechanisms of derivational morphology in remedial materials?

The aim of this preliminary study is to identify the main characteristics of the therapeutic materials designed for derivational morphology used by French-speaking SLPs. In this context, we will present the preliminary results of our study providing a critical analysis of francophone SLP tools and resources for derivational morphology.

Methodology for the evaluation of SLP tools and resources for derivational morphology 2.1 Elaboration of a two-level criterion-referenced grid for data collection

A French criterion-referenced and critical analysis grid was designed to collect and analyze data from SLP and educational resources oriented toward morphology, and specifically derivational morphology. The methodological framework combines the principles of criterion-referenced evaluation and provides up-to-date external data from interdisciplinary scientific literature (i.e. Linguistics; Evidence-Based Practice; Education; Rehabilitation Sciences; Didactics). A typology of relevant and theoretically valid criteria was defined across two distinct levels of analysis.

The first level is designed to globally evaluate the design quality of the selected materials in six domains (general criteria) (i.e Data on expertise and marketing information, Ergonomic and technical qualities, Target population, Global objectives and social validity, Theoretical validity, Measures of equipment/treatment efficacy) and 22 sub-domains (sub-criteria) (e.g. Theoretical and social validity of the materials, expertise of the authors, ergonomics, quality of the instructions, etc.).

The second level specifically evaluates the types of tasks presented as well as the (presumably) complex lexemes selected in the support materials. Ten tasks involving morphological activities were tagged using a taxonomy adapted from [START_REF] Berthiaume | L'évaluation de la conscience morphologique : proposition d'une typologie des tâches[END_REF]. A criterion checking phase was then carried out independently by two expert morphologists to adjust this taxonomy and the related concepts. The grid was constructed in April 2020 and tested with 3 β-testers and the principal designer in May 2020.

A phase of adjustment and random re-testing with the 3 β-testers took place in June 2020 with test support. The phase of analysis of the SLP resources and tools was held from June to September 2020.

Selection of SLP resources and tools

The selected materials come from francophone publishers that claim to be specialized in SLP and remedial pedagogy. The materials were identified on the web using the following descriptors: [1] morphology, [2] morphological awareness, [3] derivational morphology, [4] morphological composition, [5] spelling, [6] vocabulary. From the outset, as the identification of such resources and tools is affected by their availability, variety and online exposure, a systematic and controlled approach was not considered.

Data collection procedures

An identification number was assigned to each of the resources and tools selected for study. For each criterion, the judge's collection procedure followed detailed and systematically referenced guidelines and rationales. In practice, for the first phase of analysis, the manuals presenting the materials or the pedagogical chapters were analyzed: when available, all of the publisher's manuals, instructions, appendices and the promotional website were consulted in their entirety. Judge 1, who designed the grid, carried out an analysis on all of the materials selected for study. Judge 2 then performed a blind analysis of 20% of the data collected from 5 randomly selected sets of materials.

During the first level of the SLP materials analysis (Phase 1), judgements were weighted using a Likert Scale (see [START_REF] Likert | A technique for the measurement of attitudes[END_REF], with the weighting distributed evenly across the sub-criteria, in particular those associated with a vigilance marker. This marker highlights a key criterion in the design of a resource. To consider weighting (i.e., qualitatively assigning a grade), the judge must examine all of the sub-criteria before assigning a qualitative score to the overall criterion in the analysis table. Gradients range from 0-red (unsatisfactory) to 4-dark green (satisfies all criteria) with color coding. In Phase 1, all of the SLP resources and tools were treated consecutively. In Phase 2, a delay occurred to ensure that Judge 1 was not subject to a halo effect or contamination bias. During the second level of the SLP support analysis (phase 2), complex lexemes were collected and labelled according to i) the typology (i.e. suffixed lexeme, prefixed lexeme); ii) their base; and iii) their lexical category (i.e. noun, verb, adverb, adjective), including pseudoword. The occurrences recorded through the SLP supports were then automatically identified and filtered through the AntConc concordance software [START_REF] Anthony | AntConc: A learner and Classroom Friendly, Multi-Platform Corpus Analysis Toolkit[END_REF].

Preliminary results

Results of the general characteristics analysis of the SLP resources and tools

The results concern 15 resources designed for morphological remediation and/or learning. Only two satisfied most of the criteria in the analysis grid and none of the vigilance markers were involved in the scoring. For both of these cases, the authors were careful to systematically provide relevant and substantial information to administrators in their instruction manuals and promotional websites. Both approaches were theoretically valid and the tools had been tested beforehand in qualitative studies. The results of the general characteristics analysis of the SLP morphological resources and tools are the following:

Quality Criterion 1: Data on expertise and marketing information indicates that 53.33% (n=8) of the SLP tools achieve a grade of 3 (criterion is mostly satisfied); 40% (n=6) of the materials obtain a grade 0 (criterion unsatisfied) and 6.6% (n=1) a grade 1 (criterion poorly satisfied).

3.2

Results of the specific analysis of the complex lexemes and morphological activities from the SLP resources and tools

In the second phase, a specific analysis of the data found in the activities on derivational morphology was carried out through a static criterion-referenced grid.

Results of complex lexemes used in SLP supports

In total, the corpus of occurrences compiled from the 15 SLP activities is a collection of 8251 entries. The collected structures were automatically filtered through the AntConc concordancer software and revealed 5134 occurrences of (presumed) complex lexemes. We cite here the most frequent lexemes, i.e. those appearing 10 or more times: Among these most frequent complex lexemes, the inX pattern is overrepresented (7/15=46,67%, corresponding to 49,4% of the sum of occurrences). The two remediation activities with the best general characteristics also display this kind of over-representation. However, as has been shown [START_REF] Schwarze | La notion de règle morphologique et les mots complexes non construits[END_REF][START_REF] Dal | Adjectifs positifs en -able et négatifs en in-correspondants en français : ou pourquoi seuls sont importables les ordinateurs portables[END_REF] Moreover, three of the in-lexemes (ILLÉGAL, ILLOGIQUE, IRRÉGULIER) illustrate the phenomenon of graphic regressive assimilation: in these adjectives, the graphic consonant <n> of the prefix is assimilated to the first consonant C of the radical of the base, if C =<l> or <r>3 . However, as shown by [START_REF] Buchi | Réel, irréel, inréel : Depuis quand le français connaît-il deux préfixes négatifs in-?[END_REF], since the 17 th century, in such contexts, the prefix in-tends to be realized <in> (/ɛ /), even when an adjective containing this assimilation is lexicalized. For example, despite the existence of ILLOGEABLE, which is attested from the 16 th century, INLOGEABLE appeared in 1784, and INRETROUVABLE (1933) competes with IRRETROUVABLE (1906). A search on the web carried out on July 22, 2021 confirms this competition in contemporary French, with a preference for <in> when the prefixed adjective is not lexicalized (TLF): Tab 2: Adjectives in -able and their corresponding inX from the web

Results of the specific analysis of the morphological tasks in SLP resources

A qualitative analysis reveals that the derivation task (task. A) is the most frequently occurring task in the instructions (n=8). The main objective of this task is to test the learner's ability to produce correct derivational forms from a given base. In this activity, the teacher/SLP can observe if some previously learned strategies are transferred to similar or equivalent contexts. The labelling task called structural analysis (task. B) is the second most used type (n=4). This task involves identifying and separating the elements involved in complex lexemes. In some cases, the occurrences proposed in those resources are 1) affixed or 2) distractors (pseudo-affixed). The underlying objective is to investigate the learner's ability to analyze the structure of a given word. In order of frequency, the other types of tasks found are the affix choice task (task. G -n = 3) and the relation judgement task (task. D -n = 2). This typology still needs to be refined because the instructions in the materials are sometimes ambiguous, which hinders the process of task categorisation. In fact, the majority of SLP resources and tools (n= 9) indicated one type of task but the activity in fact belonged partially or wholly to another category. In other cases (n=2), the instructions were not precise enough to allow the judge to assign a category label. Except for the two resources that had the best general results, it was not obvious in which area of intervention and for which mode (oral or written), the SLP tools were designed to be used.

Discussion

Publishers specialising in speech and language therapy regularly produce tools and resources with a focus on derivational morphology. These products are said to be adapted for Developmental Language Disorders, reading impairments or children with dyslexia. The aim of this study was to identify the main characteristics of materials designed for remediation in derivational morphology used by Frenchspeaking SLPs. First, the results indicate that a majority of SLP tools suffer from a lack of objectivity in their theoretical constructs (operational definitions, theoretical validity, standardized terminology, and developmental knowledge) and in their measurements of social validity. Furthermore, the pedagogical objectives and application procedures of most of these materials are not transparent or are lacking in the instructions (Research Question 1).

Secondly, the instructions for morphological activities are predominantly oriented towards derivation tasks (task A) and structural analysis tasks (task B) (Research Question 3). However, for 13 out of 15 resources it was found that a majority of the tasks proposed either fit into more than one sub-task category or were not specific enough to be formally labelled. Moreover, it is not explicitly stated whether these tasks are to be performed in oral or in written form or both. This is the case for all but the two resources that score very well in their general characteristics.

Finally, an examination of the lexemes frequently used in these SLP tools shows that the occurrences analysed do not stand up to structural and diachronic examination. As said before, the morphological pattern inX is not productive in French, except with deverbal adjectives in -able. This calls into question the relevance of employing the most frequent lexemes prefixed with in-in SLP supports (Research Question 2). Lexemes such as INHUMAIN or INJUSTE, for example, are not good primary stimuli (task A). In particular, the specific analysis of the derived occurrences raises doubts about the rationales behind the two remediation resources that had the best overall characteristics. The context of the task (task A or B) seems to be in contrast with the target lexemes proposed in most of the supports. At the very least, this raises the question of whether it is in fact the morphological mechanisms that are practiced in tasks associated with this type of pattern, or rather memory skills or the breadth of the learner's mental lexicon. For the field of derivational morphology, [START_REF] Gary Libben | Psychocentricity and participant profiles: implications for lexical processing among multilinguals[END_REF] partially support this position, suggesting that a psychocentric perspective should be adopted in order to understand the role that lexical and linguistic variables can play. The psychocentric perspective emphasises the fact that notions such as the morphological structure of lexemes, their frequency or their transparency are, in their essence, abbreviated expressions of the internal cognitive states of a language user, rather than stimulusindependent features. According to this view (if it is indeed appropriate to conceptualize word recognition as a process by which a reader or listener makes sense of a read or heard stimulus), it is the internal states of the language user at the time of recognition that are, in fact, the source of the outcomes that a researcher, clinician, or educator measures as dependent variables in a psycholinguistic experiment or teaching.

The lexicon of a language such as French is largely made up of complex lexemes: prefixed, suffixed, converted or compounds. The question of lexicon invokes different cognitive and cultural representations among speakers -including SLPs and teachers -which may be motivated either by their relationship to the norm (i.e. what cognitive resources or normative tools would justify the use of a particular complex lexeme rather than another?) or by use (i.e. a complex lexeme used within a community or the spontaneous appearance of a meaningful construction). This structural information is usually obtained by speakers in the etymological sections of dictionaries. However, the variability of its formulation makes it difficult to exploit, not to say artificial, from the point of view of usage. Moreover, studies on morphological processing in French do not achieve a consensus and suffer from the same inconsistencies as those found in English [START_REF] Sánchez-Gutiérrez | Development of derivational morphological awareness in Anglophone learners of Spanish: A relational knowledge study[END_REF]. Many variables are involved and authors mention that morphological processing in French is influenced by root frequency, wordlevel morphological structures (suffixed versus prefixed words), semantic or even pre-semantic morphological processing (Colé et al., 2002;[START_REF] Beyersmann | Semantic processing during morphological priming: An ERP study[END_REF]. Some welcome efforts have been made to provide resources to investigate the morphology of French. For example, the French database DériF [START_REF] Hathout | An experimental constructional database: the MorTAL project[END_REF][START_REF] Namer | Morphologie, lexique et traitement automatique des langues : l'analyseur DériF[END_REF] describes a collection of complex lexemes, while the French-Canadian cross-linguistic database MorphoLex-Fr [START_REF] Mailhot | MorphoLex-FR: A derivational morphological database for 38,840 French words[END_REF] focuses on characterizing the different variables that impact the morphology of English and French. These types of contributions provide operational descriptions of occurrences and variables that modulate morphological processing. International scientific contributions that describe the derivational properties of words in a systematic way and anchor their approach at the interface of fundamental linguistic research, applied research and societal demand would make it possible to respond to these multiple challenges. This is in particular the case of the research project Demonext, which aims to develop a morphological database with an automated interface to empirically confirm and define hypotheses in morphology, develop tools for automatic language processing (ALP), vocabulary teaching and the treatment of developmental or acquired language disorders. To conclude, the use of a strict taxonomy of tasks and lexematic structural analysis is relevant for the analysis of clinical materials.

Conclusion

The preliminary results of this study indicate that the francophone remedial materials used by SLPs for working on morphology and derivational morphology present weaknesses in their general characteristics, in the typology of the morphological tasks provided, and in the efficacy of the choice of derivational lexemes targeted for remedial treatment. A critical analysis of materials for remedial and clinical use is relevant to enabling SLPs to make informed and evidence-based choices in their practice and in the materials they use. Additional studies should be carried out along the same lines, alongside the development of research at the interface of linguistic expertise and clinical needs.

  The results obtained indicate that 40% (n=6) of the materials are scored at grade 1. The rest are divided as follows: 20% (n=3) each at grade 0, grade 3, and grade 4. Quality Criterion 5: Theoretical validity finds a majority of materials at grade 0 with a proportion of 73.33% (n=11), 13.33% (n=2) of the tools came in at grade 2; one tool scored grade 3 and one support obtained grade 4.

	Quality Criterion 2: Ergonomics and technical qualities reveals that 46.67% (n=7) of the materials analysed obtained a grade of 0 in this category; 40% (n=6) obtained a grade of 2 or 80% of the tools rated grade 0 (n=12). One tool scored grade 2, while one tool scored grade 3. 3Quality Criterion 6: Measure of equipment/treatment effectiveness: The results indicate that Only one support achieved grade 4.

, (equal distribution), and only two tools obtained a grade of 4. Quality Criterion 3: Target Population indicates that 60% (n=9) of the tools are at grade 0, while 13.33% (n=2) are at grade 1 and grade 2. Finally, 26.67% (n=4) of the materials are rated at grade 4. Quality Criterion 4: Global objectives is dependent on Criterion 5: Theoretical validity which is found below.

  French a very long time ago: 11 th century (OUVERTURE), 13 th century (INJUSTE, LAITIER, COUPURE, IRRÉGULIER), 14 th century (ILLÉGAL, INHUMAIN), 16 th century (INCROYABLE, INCAPABLE), 17 th century (FLEURISTE), 18 th (DENTISTE, SÉCHAGE), 19 th century (GUITARISTE, PRÉHISTOIRE). Six of them have a Latin cognate (ILLÉGAL, INJUSTE, INCAPABLE, INHUMAIN, IRRÉGULIER, OUVERTURE), so it is not clear whether they were constructed in French or whether they are borrowings. These 15 lexemes correspond to six morphological patterns. Two of them are prefixations: inX (ILLÉGAL, INCROYABLE, INJUSTE, INCAPABLE, ILLOGIQUE, INHUMAIN, IRRÉGULIER), préX (PRÉHISTOIRE); the other four are suffixations: Xiste (FLEURISTE, DENTISTE, GUITARISTE), Xure (COUPURE, OUVERTURE), Xier (LAITIER) and Xage (SÉCHAGE).

	16 occ.	13 occ.	12 occ.	11 occ.	10 occ.

ILLÉGAL "illegal" INCROYABLE "unbelievable" INJUSTE "unfair" LAITIER "milkman" FLEURISTE "florist" INCAPABLE "unable" DENTISTE "dentist" COUPURE "cut" GUITARISTE "guitar player" ILLOGIQUE "illogical" INHUMAIN "inhuman" IRRÉGULIER "irregular" OUVERTURE "opening" PRÉHISTOIRE "prehistory" SÉCHAGE " drying" Tab 1: most frequent complex lexemes in the 15 SLP tools

First, we note that these 15 lexemes are highly lexicalized, and that some of them first appeared in

  , in-prefixation in French is not productive, except with deverbal adjectives in -able (e.g. DÉCHIRABLE / INDÉCHIRABLE; TRANSFÉRABLE / INTRANSFÉRABLE). Only two of the seven lexemes in in-that are frequently used in SLP, INCROYABLE and INCAPABLE, appear to have this linear structure inXable. Yet in the second, the segment cap-is difficult to relate to a French verb (CAPABLE is inherited from Latin CAPABILIS).

During reading, poor readers may have decoding difficulty, thus difficulty reading words in context accurately (see amongothers Stanovich, 1988).

From a phonological point of view, the digramm <in> is reduced to /i/ in these lexemes.

Acknowledgments

This research was supported by the Agence Nationale de la Recherche, grant number: ANR-17-CE23-0005.

The authors thank Kathleen O'Connor, Ph.D., for her help in proofreading this paper.


Hiba Abou Jamra

Marinette Savonnet
email: marinette.savonnet@u-bourgogne.fr

Éric Leclercq
email: eric.leclercq@u-bourgogne.fr

Detection of event precursors in social networks: A graphlet-based method

Keywords: Graphlets, Event Precursors, Social Networks

The increasing availability of data from online social networks attracts researchers' interest, who seek to build algorithms and machine learning models to analyze users' interactions and behaviors. Different methods have been developed to detect remarkable precursors preceding events, using text mining and Machine Learning techniques on documents, or using network topology with graph patterns. Our approach aims at analyzing social networks data, through a graphlets enumeration algorithm, to identify event precursors and to study their contribution to the event. We test the proposed method on two different types of social network data sets: real-world events (Lubrizol fire, EU law discussion), and general events (Facebook and MathOverflow). We also contextualize the results by studying the position (orbit) of important nodes in the graphlets, which are assumed as event precursors. After analysis of the results, we show that some graphlets can be considered precursors of events.

Introduction

Online social networks (OSN) play an essential role in individuals' and businesses' daily lives. Due to social interactions between individuals in these networks, scientists have an opportunity to observe and analyze increasing amounts of data to extract value and knowledge.

Disease outbreaks, environmental and industrial crises present challenges to researchers in different domains such as economy, finance, earth sciences, epidemiology, and information science. Detection of weak signals can be a key for anticipating changes in advance and avoid letting them cause surprise [START_REF] Hiltunen | Weak Signals in Organisational Futures[END_REF]. OSN enhance the emergence of echo chambers where ideas are amplified and can conduct to a digital crisis. To limit negative publicity (known as "bad buzz"), organizations should be vigilant to weak signals. Detection of significant patterns or motifs helps to understand the network dynamics and identify or predict complicated situations. Network topological properties such as density, assortativity, and degree centrality help to understand the network's global structure.

This article introduces an approach to help experts detect weak signals by topological analysis of the Twitter network. Our main contributions are 1) identification of graphlets as event precursors; 2) evaluation of the identified graphlets about their participation in the event; 3) contextualization of the results to help experts in interpretation; 4) evaluation of the proposed method using existing real data sets obtained from the Cocktail project and well-known data sets used as a benchmark. Cocktail is an interdisciplinary project aiming to develop a platform that will enable organizations to build a communication strategy, anticipate a crisis via a communication response, and adapt their industrial offers.

The rest of this article is organized as follows: Section 2 introduces some background on weak signals, event precursors, and describes similar works. In section 3, after a brief reminder on graphlets concept, we explain and illustrate the proposed method starting from time series of social networks data to event precursors identification, and the study of the correlation between precursor graphlets and the event of interest. Section 4 introduces the experimental part: it describes the main characteristics of the used data sets. In section 5, we test the proposed approach on real events based on industrial and environmental crises, along with experiments on benchmark network models to evaluate and verify this approach. Finally, conclusions and future perspectives are presented in section 6.

Related Work

In a digital society, detection of weak signals has become necessary for decisionmakers in industrial and commercial policy and communication strategy while projecting future scenarios. Weak signals can be the precursors of future events. The detection of these signals can either transform them towards a trend or an event in the future or stop their evolution for controlling and preventing future crises. Ansoff [START_REF] Igor | Managing strategic surprise by response to weak signals[END_REF] was the first to propose the concept of a weak signal for strategic planning through environmental analysis. He defines weak signals as the first symptoms of strategic discontinuities that act as early warning information of low intensity, which can be the initiator of an important trend or event. Table 1 presents terms and definitions qualifying weak signals by social scientists. Event precursors and weak signals are two concepts with strong proximity. Generally speaking, a precursor is in a relationship with the event of interest. It is any behavior, situation, or group of events that is a leading indicator of future incidents or consequential events [START_REF] William R Corcoran | Defining and analyzing precursors[END_REF]. In the following, we present several studies related to our work. We can classify these studies into three categories: 1) text mining and Natural Language Processing (NLP); 2) Machine Learning (ML) for identification and forecasting; and 3) motifs or patterns.

Many text mining and NLP approaches have been proposed, where Web documents are analyzed through a quantitative analysis of keywords. Yoon et

Source

Ansoff 1975 [START_REF] Igor | Managing strategic surprise by response to weak signals[END_REF] Godet 1994 [START_REF] Godet | From anticipation to action: a handbook of strategic prospective[END_REF] Coffman 1997 [START_REF] Coffman | Weak signal research, part I: Introduction[END_REF] Hiltunen 2010 [START_REF] Hiltunen | Weak Signals in Organisational Futures[END_REF] Welz 2012 [START_REF] Welz | Weak signals detection: Criteria for social media monitoring tools[END_REF]

              

Definitions and terms

Incomplete information, imprecise, fragmentary Low intensity, low visibility Initiator of an important event, of a future trend Low utility, meaningless when analyzed individually But can make sense if seen as a set of information al. [START_REF] Yoon | Detecting weak signals for long-term business opportunities using text mining of web news[END_REF] have proposed two indicators: the degree of visibility based on keyword frequency and the degree of diffusion based on document frequency and considering their rates of increase in time. A keyword that has low visibility and a low diffusion level is considered a weak signal. Other studies leaned on these two indicators by adding a context to a list of keywords and used, for example, topic modeling such as LDA (Latent Dirichlet Allocation) [START_REF] Krigsholm | Applying Text Mining for Identifying Future Signals of Land Administration[END_REF][START_REF] Maitre | A Meaningful Information Extraction System for Interactive Analysis of Documents[END_REF] and clustering algorithms such as k-Means or k-Medoids [START_REF] Leonardo | A Clustering Method for Weak Signals to Support Anticipative Intelligence[END_REF].

Ning et al. [START_REF] Ning | Modeling precursors for event forecasting via nested multi-instance learning[END_REF] developed a model of multiple instance learning algorithms, based on supervised learning techniques, to formulate the precursor identification and the forecasting issue. The model consists of assigning a probability to collected news articles associated with targeted events (protests in their study). The greater probability is, the more the news article is considered as a precursor containing information about this event's cause. Another study by Ackley et al. [START_REF] Ackley | A Supervised Learning Approach for Safety Event Precursor Identification in Commercial Aviation[END_REF] adopted supervised learning techniques (Random Forest and Sequential Backward Selection algorithms) in the commercial aviation operation domain to analyze and track critical parameters leading to safety events in the approach and landing phases. Furthermore, some researchers were interested in identifying specific patterns in networks, known as motifs, which could be considered as event precursors. Baiesi et al. [START_REF] Baiesi | Scaling and precursor motifs in earthquake networks[END_REF] presented a method that studies correlations within graphs of upcoming earthquakes using tools of network theory. They measured the distance between network nodes along with the clustering coefficient, which reflected intentionally basic mechanisms of seismic movements and earthquake formation/propagation. After applying statistical tools on the network topology, they found that simple motifs such as special triangles constitute an interesting type of precursors for significant events. Later on, several approaches studied the identification and the role of motifs in critical events such as crime analysis [START_REF] Davies | Event networks and the identification of crime pattern motifs[END_REF] and ongoing attacks detection [START_REF] Juszczyszyn | Motif-based attack detection in network communication graphs[END_REF].

These works aimed to identify weak signals relying on text mining techniques and network theory tools. The last one leads us to our hypothesis that graphlets, which are particular motifs, can be precursors of events. But to the best of our knowledge, there has not been a graphlet-based solution to detect event precursors in social networks and assess their relationship with the event of interest. We present our proposed approach in the upcoming section.

Graphlets as potential event precursors

The most known characteristics of weak signals are usually hard to quantify, so we prefer to rely on the notion of event precursors of small intensity to obtain a more precise definition, by considering an event as an activity peak and a precursor as a signal of lower importance or intensity, being in a correlation with the event.

Conventional methods based on simple statistical techniques are not able to identify event precursors easily. Instead, they are helpful to identify events such as the family of ARIMA, EDM, HDC algorithms [START_REF] Ray | Using statistical anomaly detection models to find clinical decision support malfunctions[END_REF]. We choose to explore another approach based on the assumption that networks' topology plays an essential role in information propagation, hence in the formation of an event, so we assume that graphlets found in social networks can be considered as potential event precursors, just as cliques are for communities. They have proven their worth in numerous contexts in network research [START_REF] Hulovatyy | Exploring the structure and function of temporal networks with dynamic graphlets[END_REF].

In this section, we investigate the following questions: Can graphlets be identified as event precursors? Can these precursors be qualified as weak signals prior to the event of interest? Before going into details, we present the essential notion of graphlets. We describe how to prepare and transform data to enumerate graphlets in a temporal graph built from interactions between users and discover the potential event precursors' graphlets.

Graphlets in a nutshell

Graphlets, first introduced by Pržulj [START_REF] Pržulj | Modeling interactome: scalefree or geometric?[END_REF], are particular types of motifs in a network, and thanks to their predefined sizes and shapes, they are easy to interpret by experts in the domain, such as social scientists or political scientists. A graphlet is a connected induced non-isomorphic subgraph (2 to 5 nodes) chosen from the nodes of a large graph. 30 graphlets from G 0 to G 29 with up to 5 nodes are possible: the G 0 graphlet of size 2, two graphlets of size 3 which are G 1 and G 2 , 6 graphlets of size 4, and 21 graphlets of size 5. Orbits, or positions, represent the equivalence classes of graphlets [START_REF] Pržulj | Biological network comparison using graphlet degree distribution[END_REF]. They are the positions to which nodes belong in the 30 graphlets; nodes belonging to the same orbit are interchangeable. For example, the star-shaped G 4 graphlet 7 6 consists of two positions; one of them is central (orbit 7) occupied by one node, and the other is peripheral (orbit 6) and shared between the remaining three nodes that are interchangeable.

There exist several algorithms to enumerate graphlets and orbits of a graph. A survey was made by Ribeiro et al. in 2019 [START_REF] Ribeiro | A survey on subgraph counting: concepts, algorithms and applications to network motifs and graphlets[END_REF], in which they provided an overview of the existing algorithms for subgraph counting, classified these algorithms, and highlighted their main advantages and limitations. They explored the methods for counting subgraphs from three perspectives: 1) exact counting algorithms (e.g., ESU/FANMOD, RAGE, Orca); 2) approximate counting algorithms (e.g., ESA, RAND-ESU ); 3) parallel processing algorithms (e.g., DM-ESU, GPU-Orca). The survey provides valuable insight from a practical point of view of the algorithms and their existing implementations with a trade-off between accuracy and execution time.

To choose the most convenient algorithm for counting graphlets and orbits in the studied graph structures, we have defined 3 essential criteria: 1) exact counting of graphlets that are up to five nodes, to maintain the interpretability of the results; 2) orbits counting for the study of nodes positions within each graphlet; 3) availability of source code. We rely on the Orca algorithm proposed by Hočevar and Demšar in 2014 [START_REF] Hočevar | A combinatorial approach to graphlet counting[END_REF], which is an exact counting algorithm, coming from an analytic approach based on matrix representation, and works by setting up a system of linear equations per node of the input graph that relate different orbit frequencies. It counts small subgraphs up to 5 nodes and focuses on orbits counting. Considering e as the number of edges and d the maximum degree of nodes, its time complexity is of O(ed) for four-node graphlets and O(ed 2 ) for five-node graphlets. We performed an experimental analysis to evaluate Orca's implementation complexity. With up to 15 000 edges in a graph, the calculation time is less than 5 seconds, but it reaches 6 hours with up to 160 000 edges. Figure 1 shows execution time based on the number of edges.

Proposed method

We propose a graphlet-based analysis method that facilitates the results' interpretation. Once the potential precursors have been revealed, it is still necessary to validate the fact that they are weak signals, determine their link with the studied event, and then allow experts to understand their role. We prove experimentally that these graphlets can be event precursors. We present our method consisting of six steps, depicted in figure 2. 0. The first step is to build a time series from social networks data. Once raw data is collected, for example, tweets in JSON format, some interactions Fig. 2. Outline of the method of interest are selected (e.g., retweet, quote, mention), and a graph structure is generated as a tuple with three components representing interactions between entities at a given date (e.g., (user1,user2,124354432)). A time series X is created from the number of interactions selected between all pairs of nodes. It is a sequence of n elements X = (x i ) 1≤i≤n = (x 1 , x 2 , . . . , x n ). A method to remove the seasonal part from the original time series is then applied [START_REF] Brownlee | Introduction to time series forecasting with python: how to prepare data and develop models to predict the future[END_REF]. We consider an event as an activity peak resulting from a variation in the interactions between entities, and the peak is identified either manually or by event detection algorithms. Before and during the event, the time series is divided into snapshots S t according to the duration or importance of the event (e.g., a day, 12 hours, 6 hours, 1 hour), in a way to have sub time series of the original series: S t = (x i ) t≤i<t+d with d the constant duration of a snapshot, and the constraint that all the S t form a partition of the original series X.

1. Enumeration of graphlets for each snapshot determines a topological signature before and during the event. Snapshots S t are represented as components of a numerical vector (G t 0 , G t 1 , . . . , G t 29 ), G t x is the number of graphlets of type x in the snapshot S t . We rely on the Orca algorithm1 , it provides an acceptable runtime as all snapshots contain at most a few thousand edges (see figure 1). 2. We apply a normalization procedure on these vectors to re-scale their values to a particular magnitude for further measuring and This step is of significant importance as it should not hide small signals but instead make them comparable to others. The procedure relies on a framework proposed by D.Goldin and P.Kanellakis [START_REF] Dina | On similarity queries for time-series data: constraint specification and implementation[END_REF] in which they study the similarity between two queries relating to a temporal database. Two real numbers a and b define a transformation T a,b on X by joining each x i with a × x i + b. X represents the normal form of X calculated by:

X = T -1 σ,µ (X) = T 1 σ ,-µ σ (X)
in which µ(X) = 0 and σ(X) = 1, µ is the mean and σ the standard deviation. Therefore, the mean of each graphlet type G x for all snapshots is calculated as:

µ(G x ) = 1 s s t=1 (G t x ) ∀x ∈ {0, . . . , 29}, s is the number of snapshots
Then the standard deviation is calculated as:

σ(G x ) = s t=1 (G t x -µ(G x )) 2 s -1 ∀x ∈ {0, . . . , 29}
By applying this normalization procedure for each of the snapshots S t , each component of its vector G t x is normalized by:

G t x = (G t x ) -µ(G x ) σ(G x )
3. From the normalized values obtained, the evolution of all the vector components is studied via the calculation of their velocity and acceleration, with the purpose to highlight the graphlets that come out quickly before the other types. The calculation of these attributes is as follows:

-Velocity:

V t x = G t+1 x -G t x ∀x ∈ {0, . . . 29} -Acceleration: A t x = ∆V x ∆t = V t+1 x -V t x ∀x ∈ {0, . . . 29}, ∆t = 1 between snapshots 4.
We observe the obtained results in steps 2 and 3 to capture significant variations in their values before the activity peak. We choose the k graphlets with the highest velocity and acceleration values as potential precursors of events.

5. This step aims to validate the potential precursors' graphlets by eliminating those irrelevant (false positives) and maintaining the pertinent ones supposed as weak signals (true positives). Although keeping some false positives can help social scientists to examine the information behind critical situations. It is composed of two stages: 1) we evaluate cross-correlation between each precursors' graphlet time series and the original interactions time series, and 2) for correlated graphlets, we quantify their contribution to the global evolution of graphlets to confirm if they are weak signals or not.

Cross-correlation2 is used to validate the intrinsic properties of the method.

It is a linear measure of similarities between two time series X and Y , which helps evaluate the relationship between two series over time [START_REF] Brian | Modern applied statistics with S. springer[END_REF]. An offset/lag h is associated with this measure, knowing that if h < 0 then X could predict Y , and if h > 0 then Y could predict X. Weak signals selection is a simple ratio calculation that measures the correlated graphlets' contribution to the global evolution of graphlets for the studied period. From the correlated graphlets found, the total number of a graphlet type x in all snapshots is divided by the total number of graphlets for all snapshots, as follows:

R(G x ) = s t=1 (G t x ) T (G) and T (G) = s t=1 ( 29 x=0 (G t
x )), with s the number of snapshots. The resulted ratios R, are sorted in ascending order, to verify if the identified correlated graphlets remain at the top of the list; if so, they are qualified as weak signals, the other graphlets are eliminated.

6. This step aims to provide adequate analysis elements to domain experts to interpret the previous steps' obtained results and respond to potentially critical situations. For each orbit (i.e. the position, or the node's role in the graphlet) of graphlets considered as weak signals, we count how many times nodes of the initial graph appear in these graphlets. To restrict the information to study and facilitate the interpretation, we consider only the most influential nodes, hence the PageRank algorithm is used to help to identify these nodes in the graph.

Data description

We describe in this section the data sets used for our experiments. To this end, the selected data sets include a sequence of temporal interactions between users. Two first data sets represent real case scenarios, and the other two sets3 are social benchmark networks used to confirm our method.

Twitter -Lubrizol fire: This network contains tweets published after a fire broke out at the Lubrizol factory in Rouen-France. From the raw Twitter data, the corpus contains tweets between midnight of October 28, 2019, and midnight of October 30, 2019. The reduced corpus consists of 18,914 tweets, 12,187 of these tweets are original, and 1,984 include mentions which are the interaction type studied in this example.

Experiments, results and discussion

In this section, we present experiments that aim to validate the proposed method by detecting graphlets that are supposed to be weak signals, supplemented by contextualization elements so that experts can trigger actions. We apply experiments on four different data sets, two of them are the subject of critical situations in industry and agriculture, and the remaining ones belong to random events. The Cocktail platform collected the first two data sets, domain experts provided the accounts and keywords needed for the collection. The two other data sets were used to validate the approach.

Industrial crisis: Twitter -Lubrizol fire

The first experiment of the proposed approach was carried out on the Twitter Lubrizol network. Our event of interest is the unexpected visit of President Macron to Rouen, October 30, 2019, around 6 p.m. Therefore, the study period is reduced to two days before the event (28 and 29), along with the event's day (30). After step 0, we obtain a temporal graph that contains 2,231 nodes and 3,821 edges4 .

We choose to work with snapshots of one hour, to capture the biggest number of graphlet types, especially those with complex shapes, which will help with a finer interpretation of the results. The graphlets number is calculated for each snapshot and the resulting values are normalized. Next, velocity and acceleration are measured for the normalized graphlet values. After analysis of the three computed attributes, we notice an increase in certain graphlets' number and velocity on October 30 starting at 4 p.m., like G 2 , G 5 , G 8 and G 27 . Therefore, we consider these graphlet types as potential event precursors. Table 2 presents graphlets number, velocity, and acceleration results for certain graphlet types, for the snapshots corresponding to three hours before the event. It compares the evolution of the attributes mentioned above between the graphlets that evolved starting at 4 p.m. (supposed precursors), and other graphlets that did not show remarkable variations for the same snapshots. The notable changes in attributes' values are highlighted in blue. We notice that other graphlet types like G 4 and G 11

, start increasing from 6 p.m, which is the snapshot of the activity peak, and hence they are aligned with the event.

Table 2. Enumeration results of some graphlets before the event. The highlighted values correspond to the potential precursor's graphlets S t : 30/10 3p.m-4p.m S t+1 : 30/10 4p.m-5p.m S t+2 : 30 Next, we validate the potential precursors and select the ones supposed to be weak signals. We apply cross-correlation between the initial time series and the ones belonging to precursor graphlets. The time series of G 2 , G 5 , G 8 and G 27 present correlations with a positive lag h of one and two hours with the initial time series, having significant values equal to 0.8, which indicates that the number of interactions in the initial series follows with a lag of 1 or 2 hours the number of graphlets. The calculated ratios highlight the weak presence of these correlated graphlets in the rise of mentions number, compared with other strong graphlets like G 11 , hence G 2 , G 5 , G 8 and G 27 are considered weak signals. A fine-grained experiment is carried out to contextualize the obtained results in the previous steps: we calculate the number of times an influential node is in an orbit of a selected graphlet. We find users like manon leterq and massinfabien journalists, and 76actu the local information site, having a rise in the number of orbits of the selected graphlets, starting at 4 p.m (two hours before the event). Table 3 presents an extract of the number of times the above influential users appear in the orbits of graphlets G 2 (O 3 ) and G 27 (O 68 and O 69 ). The remarkable increase in values is highlighted in blue. We did the same calculations with a user-chosen randomly OTT 44380; the results show that he appears little in the graphlets.

/10 5p.m-6p.m Graphlet G t x V t x A t x G t x V t x A t x G t x V t x A t x
We repeat the same experiment on different time windows. In the 6-hours snapshot, we were able to extract the same graphlets; on the contrary, we could not confirm the exact time of their appearance due to the window's large size. A finer study on 30 and 15 minutes snapshots (containing fewer edges) led to a partial vanishing of complex graphlets like G 8 and G 27 over time. The absence of these complex graphlets results in information loss and makes decision-making more difficult. We rely on providing enough information to the experts to take preventive actions. , and G 28 as potential event precursors, due to the rise of their values between one and three hours before the event.

Other graphlet types such as G 9 arise in parallel with the event. The cross-correlation applied to precursor graphlets shows on the one hand that G 15 and G 21 present a positive correlation of two and three hours lags, respectively, with the initial time series, having values equal to 0.7 and 0.6 respectively (see Figure 4). G 18 also shows a positive correlation of one hour lag with a value equal to 0.7. On the other hand, G 28 did not present a positive correlation with the initial time series, hence it is not a weak signal. Furthermore, ratios are calculated for the correlated graphlets. These graphlets have low ratios compared with other graphlets types, so they are considered weak signals.

In the last step, orbits in graphlets are enumerated for the identified influential users. The results show that, for instance, a user like pcanfin (Chair of the environment committee of the European Parliament) appears for the first time at 10 a.m of the event day, in orbit O 51 of graphlet G 21 Then the number of orbits of these users starts decreasing towards the event. These users interacted against the law a few hours before the announcement of the council's decision, and their positions in these graphlets (closed connected structure) above can reveal their role in a strongly connected

Random Events: MathOverflow and Facebook

The objective of these two experiments is to verify and validate the proposed method in terms of reproducibility and results interpretation. Our method is applied to the benchmark network models MathOverflow and Facebook interactions. The event is unknown here, so we select a peak activity in the corresponding time series of each network and consider it as the event of study, to evaluate the previously obtained results for Twitter network data. We also work by snapshots of one hour.

The peak selected from the MathOverflow time series (the graph contains 414 nodes and 966 edges) belongs to activity on October 29, 2010, at 11 p.m. Enumeration results show remarkable variations in certain graphlets numbers on October 29 starting at 10 p.m. We find graphlets G 3 , G 9 and G 10 increasing first follows them the G 2 at 11 p.m., time of the peak activity. After that time, the calculated numbers start decreasing accordingly. The correlation study was not able to find positive correlations between the time series of these graphlets and the initial time series before the peak. Moreover, the enumeration of orbits in the last step was not entirely relevant since the nodes belong to anonymous users, hence the results cannot be reflected into real scenarios for analysis.

Furthermore, the Facebook data set was studied by snapshots of one hour to ease the discovery of significant precursors before the selected peak activity in the related time series (the graph contains 6,726 nodes and 6,677 edges). The peak corresponds to an event on January 07, 2009, starting at 6 a.m. We notice a prominent rise in numbers of G 2 and G 6 in the evening before the peak activity, at 6 p.m. Just as the Mathoverflow dataset, these identified graphlets can not be considered as event precursors as they did not show significant correlation results with the initial time series.

The obtained results in these benchmark networks lead us to the interpretation synthesis that these are too generalist data sets, and we can track no targeted event in the real world. Moreover, we could not identify weak signals, since most of the graphlets participate strongly in the rise of interactions between users. Data and experimental programs are available under https://github.com/hibaaboujamra/EventPrecursorsGraphlets.

Conclusion and perspectives

We have studied the hypothesis of discovering whether the graphlets are precursors for occurring events, and developed a method to evaluate and confirm this hypothesis. The proposed approach allows identifying graphlets as precursors of events and targeting those that constitute weak signals.

We performed quantitative and qualitative analyses using graph enumeration and correlation measures. The experimental results confirm that our method was able to identify event precursors and target those that can be weak signals two hours before an event.

Moreover, the last step of contextualization provides rich elements to domain experts for further analysis and interpretation of the results to react accordingly in case of critical situations.

In future works, we want to extend the experiments to other types of networks as the hashtags co-occurrence, for example, and larger networks, and automate all the method's steps. We observed experimentally that graphlets are good precursors of events, hence we attach currently to establish proof of causality between these graphlets and the event, through statistical methods like the causal inference or the Granger causality. Further investigations will consider iterating our method to eliminate nodes continuously from graphs to decrease certain graphlets' predominance and allocate the space to discover other graphlet types as event precursors to obtain a hierarchical graphlet decomposition.

Fig. 1 .

 1 Fig. 1. Experimental evaluation of Orca's complexity

Figure 3

 3 Figure 3 represents some of the considered weak signals' time series, compared to the initial mentions time series.

Fig. 3 .

 3 Fig. 3. Initial mentions time series vs. G8 and G27 graphlets time series, considered weak signals

  11 a.m in orbit O 34 of graphlet G 15 34 . Another user, TheProgressives (representing Socialists and Democrats Group in the European Parliament), appears at 10 a.m in the orbits of G 15 , G 18 and G 21 , but shows up strongly at 11 a.m in orbits O 34 and O 51 .

Fig. 4 .

 4 Fig. 4. Correlation of G15 with the initial mentions-retweet time series

Table 1 .

 1 Weak signals definitions and terms

  Twitter -European CAP Law: This dataset contains tweets published in conjunction with the European Council meeting held on October 20, 2020, that lead to the announcement of the Common Agricultural Policy Law (CAP). The dataset comprises tweets collected from midnight of October 17, 2020, to midnight of October 20. It consists of 4,679 tweets, from which 807 are original, and 3,872 include mentions and retweets, which are the interaction type we study in this experiment. MathOverflow Network: This network contains temporal user interactions from the Stack-Exchange site "Math-Overflow" consisting of three interaction types: 1) answering a question; 2) commenting on another user's question; 3) commenting on another user's answer. The used data set is extracted from the original sample, and consists of 1,400 relations from October 27, 2010, till October 30, 2010. Facebook Network: This is a network representing a subset of posts to other user's walls on Facebook. The raw data sample is collected from October 2004 to January 2009, and we minimize the set to include the detailed relations between 05 January 2009, and 07 January 2009, consisting of up to 8,790 interactions between users.

Table 3 .

 3 Extract of influential users and their orbits enumeration results for some of the precursor graphletsIn this experiment, we are interested in the mentions and retweets published after European Council meetings held in late October 2020 for negotiation on the post-2020 Common Agricultural Policy (CAP) reform package, which later initiated an agreement on the proposed CAP project. The event corresponds to the 20th of October at noon, where the European Council took a position towards the CAP project. Thus, we focus on the two days preceding the event(18 and 19) and the day of the event[START_REF] Ray | Using statistical anomaly detection models to find clinical decision support malfunctions[END_REF]. The initial graph contains 2,535 nodes and 7,897 edges. The corresponding time series of the interactions is created and divided into snapshots of one hour each. The enumeration of graphlets and the calculation of velocity and acceleration allow us to extract the most pertinent graphlets G 15 , G 18 , G 21

		S t : 30/10 3p.m-4p.m S t+1 : 30/10 4p.m-5p.m S t+2 : 30/10 5p.m-6p.m
		3	69	69	3	69	69	3	69	69
	User	O3	O68 68	O69 68	O3	O68 68	O69 68	O3	O68 68	O69 68
	OTT 44380	0	0	0	0	0	0	7	18	3
	manon leterq	0	0	0	12	72	0	20	68	0
	76actu	0	0	0	31	18	49	30	5	70
	massinfabien	0	0	0	10	63	0	6	32	0
	5.2 Environmental crisis: Twitter -CAP Law

https://rdrr.io/github/alan-turing-institute/network-comparison/src/R/ orca interface.R

implemented with the R package tseries: https://www.rdocumentation.org/ packages/tseries/versions/0.1-2/topics/ccf

https://snap.stanford.edu/data/#socnets

The difference between the number of tweets in section 4 and the number of nodes and edges is since several tweets can produce the same interaction.

Acknowledgments

This work is supported by the program "Investissements d'Avenir", ISITE-BFC project (ANR contract 15-IDEX-0003), https://projet-cocktail.fr/


Methionine sulfoxide reductases: redox control of protein function and protection against oxidative stress.

Grenoble, du 11 au 13 Octobre 2021 Campus de l'Université Grenoble Alpes RÉSUMÉ À ADRESSER A ibs.evt.club-oxydase@ibs.fr Le résumé doit être dactylographié dans le cadre ci-dessous avec la police Times Roman 11 en interligne simple. La totalité du résumé doit être incluse dans l'espace prévu. Il doit être structuré de la façon suivante en commençant chaque fois par une nouvelle ligne :

 Titre : centré en minuscule et en gras, sans abréviation [START_REF] Lowther | The mirrored methionine sulfoxide reductases of Neisseria gonorrhoeae pilB[END_REF] . MsrA can act similarly on the free amino acid and oxidized proteins, whereas MsrB reduces efficiently only protein-bound MetO 2 . Another type of thiol-oxidoreductase, the freemethionine-R-sulfoxide reductase (fRMsr), identified in prokaryotes and fungi [START_REF] Hage | Distribution of methionine sulfoxide reductases in fungi and conservation of the free-methionine-Rsulfoxide reductase in multicellular eukaryotes[END_REF] , reduces the R diastereomer of the free amino acid MetO only [START_REF] Lin | Free methionine-(R)-sulfoxide reductase from Escherichia coli reveals a new GAF domain function[END_REF] . These MetO reducing enzymes are partners of thioredoxins and glutaredoxins, from which they receive reducing power. All these Msrs play important roles in the protection of organisms against oxidative stress through two main functions: i) the repair of oxidized proteins, and ii) an antioxidant function through reactive oxygen species scavenging by cyclic Met oxidation and reduction [START_REF] Lourenço Dos Santos | The Oxidized Protein Repair Enzymes Methionine Sulfoxide Reductases and Their Roles in Protecting against Oxidative Stress, in Ageing and in Regulating Protein Function[END_REF] . Moreover, the reversible Met oxidation was shown to act as a post-translational modification responsible for the activation of enzymes and transcription factors or the regulation of protein-protein interactions [START_REF] Lee | MsrB1 and MICALs regulate actin assembly and macrophage function via reversible stereoselective methionine oxidation[END_REF] . Some bacteria possess molybdenum-containing enzymes that reduce MetO, either in the free or protein-bound forms. Among these, the periplasmic MsrP reduces efficiently protein-bound MetO without stereospecificity [START_REF] Gennaris | Repairing oxidized proteins in the bacterial envelope using respiratory chain electrons[END_REF] . After a journey in the Msr world, I will present you the latest results about the periplasmic systems of MetO reduction in the purple bacteria Rhodobacter sphaeroides [START_REF] Tarrago | Rhodobacter sphaeroides methionine sulfoxide reductase P reduces R-and S-diastereomers of methionine sulfoxide from a broad-spectrum of protein substrates[END_REF][START_REF] Tarrago | Reduction of Protein Bound Methionine Sulfoxide by a Periplasmic Dimethyl Sulfoxide Reductase[END_REF] .

COMMUNICATION 2021 CLUB OXYDASE Methionine sulfoxide reductases: redox control of protein function and protection against oxidative stress.

  Methionine, either as a free amino acid or included in proteins, can be oxidized into methionine sulfoxide (MetO), which exists as R and S diastereomers. Almost all characterized organisms possess thiol-oxidoreductases enzymes named methionine sulfoxide reductases (Msr) to reduce MetO back to Met. MsrA and MsrB reduce the S and R diastereomers of MetO, respectively, with strict stereospecificity

	Lionel Tarrago
	INRAE -Biodiversité et Biotechnologie Fongiques (BBF), UMR 1163, Polytech Marseille, 163
	Avenue de Luminy, CP925, 13288, Marseille.
	Email: lionel.tarrago@inrae.fr
	 Auteurs : prénoms et noms en entier, en minuscule, maigre et justifié
	 Adresse : en minuscule maigre et justifié incluant l'adresse E-mail
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	Auteur correspondant :
	Nom : Tarrago	Prénom : Lionel
	Titre et fonction : Dr, Chargé de recherches
	Institution : INRAE -Biodiversité et Biotechnologie Fongiques (BBF)
	Adresse : UMR 1163, Polytech Marseille, 163 Avenue de Luminy, CP925
	Code postal : 13288	Ville : Marseille

Pays : France E-mail : lionel.tarrago@inrae.fr Tél : Fax :

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