Mouna Kamel
email: mouna.kamel@irit.fr

Cassia Trojahn

Adel Ghamnia

Nathalie Aussenac-Gilles

Cécile Fabre

A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages

Keywords: Distant learning, hypernym relation, knowledge extraction, Wikipedia, knowledge bases

Extracting hypernym relations from text is one of the key steps in the automated construction and enrichment of semantic resources. The state of the art offers a large varierty of methods (linguistic, statistical, learning based, hybrid). This variety could be an answer to the need to process each corpus or text fragment according to its specificities (e.g. domain granularity, nature, language, or target semantic resource). Moreover, hypernym relation may take different linguistic forms. The aim of this paper is to study the behaviour of a supervised learning approach to extract hypernym relations whatever the way they are expressed, and to evaluate its ability to capture regularities from the corpus, without human intervention. We apply a distant supervised learning algorithm on a sub-set of Wikipedia in French made of disambiguation pages where we manually annotated hypernym relations. The learned model obtained a F-measure of 0.67, outperforming lexico-syntactic pattern matching used as baseline.

Introduction

Extracting hypernym relations from text is one of the key steps in the automated construction and enrichment of semantic resources, since this kind of relation provides the hierarchical backbone structure which allows for entity type assignment. Several hypernym extraction methods have been proposed in the literature, trying to better identify the different ways this kind of relation is expressed in written natural language.

While linguistic methods rely on lexico-syntactic patterns used to identify clues of relation between terms [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF] , statistical methods, which are predominant, rely on distributional spaces 2 or on supervised [START_REF] Pantel | Ontology learning and population: Bridging the gap between text and knowledge[END_REF] or non-supervised [START_REF] Banko | Open information extraction from the web[END_REF] learning methods. Most of these technique apply on well-written text (i.e., where the language syntactic structure is wellformed), without taking into account its structure. However, some of them exploit specific textual structures, e.g. definitions [START_REF] Malaisé | Detecting semantic relations between terms in definitions[END_REF] , enumerative structures [START_REF] Fauconnier | Discovering Hypernymy Relations using Text Layout (regular paper)[END_REF] or elements like infoboxes, categories or links [START_REF] Morsey | Dbpedia and the live extraction of structured data from wikipedia[END_REF] in the case of Wikipedia.

These methods have been developed for corpora with their own specificities, e.g., domain granularity (general or specific), corpus gender (encyclopedic, scientific, journalistic, etc.), language, explicitness of the text structure (structured, semi-structured or unstructured). Another design parameter is the intended aim (i.e. extracting linguistic relations or formal triples, annotating text or populating a knowledge base). When relations have to be included in a semantic resource, the nature of this resource (thesauri, termino-ontologies or heavy ontologies) also impacts the extraction process. In addition, whatever the input corpus, hypernym relations may be expressed in different forms.

In this paper we study the behaviour of a distant supervised learning approach [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF] on a corpus where the hypernym relation is expressed in different forms. Supervised learning algorithms carry out classification based on features of the entities to be classified. When applied to text, these features include various linguistic clues (either syntactic, semantic, lexical, visual, structural, distributional clues). Training the algorithm requires the corpus to be annotated with examples of class to be learned, which is complex and time consuming. Distant supervision overcomes this limitation by relying on an external semantic resource that is mapped to the corpus to automatically generate relation annotations. Being free of manual annotation, this approach can easily be applied to any corpus with regular structures.

This work is carried out within the SemPedia1 project whose goal is to enrich DBPedia2 for French, by specifying and implementing a set of new Wikipedia extractors dedicated to the hypernym relation. We focus on French because semantic resources targeting this language are scarce. The French DBPedia resource is 20,000 times poorer than DBPedia in English. Then, we built a corpus made of Wikipedia disambiguation pages in French. These pages list the Wikipedia articles whose title is ambiguous, and give a definition of each accepted meaning. Therefore, they are rich in named entities and in hypernym relations expressed through textual definitions and entity types. Moreover, we observed that pages are structured at various degrees depending on the language. For example, pages in English are very highly structured and mostly contain "low-written text" (e.g., opposed to well-written text), where the structure substitutes the lack of full syntax and expresses a good part of the text meaning. The French pages, on the contrary, more readily mix written text and low-written text. So they are a favorable case of relation-rich pages.

Concerning the automatic annotation, we used BabelNet 9 , a knowledge resource which is (partially) derived from the training Wikipedia corpus, as recommended by the distant supervision approach. We compare our approach to a symbolic one based on lexico-syntactic patterns, which have been largely used to identify hypernym relations, in particular, on corpora rich in definitions [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF] , such as the corpus we used in our experiments.

This paper is structured as follows. Section 2 deals with the main related work. Section 3 introduces the distant supervised learning approach and the Maximum Entropy algorithm that we use. Then we present the hypernym relation learning task in Section 4. Section 5 describes the experiments and discusses the obtained results. Finally, Section 6 concludes the paper and draws perspectives for future work.

Related work

In the field of relation extraction, the pioneering work of the linguistic methods is that of Hearst 1 which defined a set of lexico-syntactic patterns specific to the hypernym relation for English. This work has been adapted to French in order to identify different types of relations [START_REF] Séguéla | Extraction de relations sémantiques entre termes et enrichissement de modèles du domaine[END_REF] , hypernym relations between terms [START_REF] Morin | Automatic acquisition and expansion of hypernym links[END_REF] or meronymic relations [START_REF] Berland | Finding parts in very large corpora[END_REF] . Moreover paterns have been progressively learned from text thanks to learning techniques.

With respect to statistical approaches, Snow and colleagues [START_REF] Snow | Learning syntactic patterns for automatic hypernym discovery[END_REF] and Bunescu and colleagues [START_REF] Bunescu | A shortest path dependency kernel for relation extraction[END_REF] applied supervised learning techniques on a set of manually annotated examples. Because the cost of manual annotation is one of the main limitations of supervised learning, distant supervision learning consists in building the set of examples thanks to an external resource [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF] . Distant supervision avoids the manual annotation phase by matching relations from the external knowledge base on the corpus. This process allows to automatically annotate relation occurrences that will become learning examples. Another way to avoid manual annotation has been proposed by Brin 15 who uses a selection of patterns to construct the set of examples, thanks to a semi-supervised learning method called bootstrapping. Agichtein and Gravano [START_REF] Agichtein | Snowball: Extracting relations from large plain-text collections[END_REF] , and Etzioni and colleagues [START_REF] Etzioni | Web-scale information extraction in knowitall[END_REF] have used this method and added semantic features to identify relations between named entities. IAn alternative technique is unsupervised learning based on clustering techniques. Yates and colleagues [START_REF] Yates | Textrunner: open information extraction on the web[END_REF] and Fader and colleagues [START_REF] Fader | Identifying relations for open information extraction[END_REF] implemented unsupervised learning and used syntactic features to train their classifiers on relations between named entities. Some of these works are also based on distributional analyses 2 20 . To better understand how to take into account the specificities of the corpora, Yap and Baldwin 21 studied the impact of the corpus and the size of training sets on the performance of supervised methods for the extraction of different types of relation (hypernym, synonymy and antonymy). Granada [START_REF] Granada | Evaluation of methods for taxonomic relation extraction from text[END_REF] compared the performance of different methods (patterns-based, head-modifier, and distributional ones) for the task of hypernym relation extraction on various kinds of corpora (encyclopedic, journalistic) in several languages.

Relation extraction can also take advantage of the page layout in two different ways. The first one relies on documents written in a markup language. The semantics of the tags and their nested structure maybe exploited for the identification of relations. A collection of XML documents has been used to build ontologies 23 24 , while a collection of HTML or MediaWiki documents has been processed to build taxonomies [START_REF] Sumida | Hacking wikipedia for hyponymy relation acquisition[END_REF] . The second category of approaches bears on specific documents or parts of them, for which the layout defines a precise semantics, such as dictionaries and thesaurus [START_REF] Jannink | Thesaurus entry extraction from an on-line dictionary[END_REF] or specific and well localized textual structures such as tables, categories 27 28 or infoboxes [START_REF] Auer | Dbpedia: A nucleus for a web of open data[END_REF] from Wikipedia pages. Any of these textual structures can also be made explicit thanks to a markup language.

Finally, as our study focuses on the Wikipedia corpus, the extracted relations could be exploited to enrich DBPedia. Several tools, called "extractors" have been developed to analyze each type of structured data in Wikipedia. Morsey and colleagues 7 developed 19 of such extractors that produce a formal representation of entities and relations identified within various structural elements from Wikipedia: abstracts, images, infobox, etc. Other works have targeted specific relations, mainly hypernym relations. For example, Suchanek and colleagues 28 used the 'Category' hierarchy of Wikipedia to build hypernym relations in the Yago knowledge base. Kazama and Torisawa 30 exploited the 'Definition' part of the pages, whereas Sumida and Torisawa [START_REF] Sumida | Hacking wikipedia for hyponymy relation acquisition[END_REF] extracted knowledge from the menu items. Recent works proposed the automatic creation of MultiWiBi [START_REF] Flati | Multiwibi: The multilingual wikipedia bitaxonomy project[END_REF] , an integrated bitaxonomy of Wikipedia pages and categories in multiple languages. Still, relation extraction from the text in Wikipedia pages has been little used to feed DBPedia [START_REF] Rodriguez-Ferreira | Improving information extraction from wikipedia texts using basic english[END_REF] . Hence, most of the knowledge from these pages remains unexploited. This lack is even more important for pages in French.

This context led us to define methods that could extract knowledge from the text in French Wikipedia pages. As a first step towards this goal, we target the extraction of hypernym relations whatever the way they are expressed, be it in well-written or in low-written text. Given the size of the corpus and the cost and expertise by the manual annotation of examples, we apply a distant supervised learning algorithm. We also decided to combine various features, at different linguistic levels (from morphology and syntax to discourse and layout). This approach can be carried out on any corpus presenting structural and/or linguistic regularities, such as web documents, and it is language-independent.

Background

Distant supervision learning

Distant supervision learning 33 , 8 refers to learning algorithms where the training examples are automatically collected using a knowledge base. The set of examples is built by aligning the knowledge base to a corpus. The resulting alignments (or text annotations) and their features are then used to train the system and learn relations. The learning ground is based on the hypothesis that "if two entities participate in a relation, all sentences that mention these two entities express that relation". Although this hypothesis seems too strong, Riedel and colleagues [START_REF] Riedel | Modeling relations and their mentions without labeled text[END_REF] show that it makes sense when the knowledge base used to annotate the corpus is derived from the corpus itself. Mintz and colleagues [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF] use Freebase as external resource. For every pair of entities linked in Freebase and appearing together within a sentence, a positive learning example is built, i.e., the learning features are extracted from the sentence and added to a feature vector for that entity pair. The set of feature vectors feed a multi-class logistic regression classifier. While Mintz and colleagues [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF] consider several relations at once with a multi-class classifier, given the size of our corpus, we only focus on the hypernym relation and a binary logistic regression classifier, the Maximum Entropy Algorithm.

The Maximum Entropy algorithm

To perform the binary classification task (isA or not-isA classes), we chose the Maximum Entropy classifier (Max-Ent) [START_REF] Berger | A maximum entropy approach to natural language processing[END_REF] which is relevant when the conditional independence of the features can not be assured. This is particularly true in NLP where features are usually words which obviously are not independent in their use (they are bound by syntactic and sematic rules). Furthermore, MaxEnt allows the management of a great number of features. It relies on the maximum entropy principle. Hence, it requires to define a set of constraints for each observation and to choose the distribution which maximizes the entropy while remaining consistent with the whole set of constraints [START_REF] Jaynes | Information theory and statistical mechanics[END_REF] . In this context of optimisation under constraints, it is mathematically proved that a unique solution exists and that an iterative algorithm converges towards this solution [START_REF] Ratnaparkhi | Maximum entropy models for natural language ambiguity resolution[END_REF] .

The classical formula of MaxEnt is the following :

P(y|x) = 1 Z exp        i w i f i (x, y)       
where P(y|x) gives the probability that the individual x (here a relation) belongs to the class y (here isA or not-isA classes). Each individual is encoded as a feature vector. The function f i is a function called feature which determines the constraints of the model. The weights w i associated to each feature account for the probability to belong to a class. Z is a normalization constant which ensures that the sum of probabilities of one individual is equal to 1.

To estimate the parameter values ŵ, we use the likelihood function that aims at determining the best estimators: ŵ = argmax j log(P(y j |x j ))

where the (x j , y j ) belongs to the set of training data. In our work, we used the OpenNLP (version 1.5.0) implementation of the MaxEnt algorithm3 .

Hypernym relation learning task

In this section, we firstly describe the resources we used for training the models. Then, these models are presented.

Resources

We use the Wikipedia sub-corpus composed of all French disambiguation pages. These pages list the Wikipedia articles whose title is ambiguous, and give a definition of each accepted meaning. Therefore they are rich in named entities and in hypernym relations. Relations are expressed in different textual structures, usually established by the guidelines of the drafting charter. These HTML pages are semi-structured textual documents that combine different levels of text structuring, translated by typographical and layout features. The combination of these feature lead to a large variety of possibilities to express hypernym relations. Figure 1 presents the Mercure disambiguation page [START_REF] Banko | Open information extraction from the web[END_REF] , where hypernym relations are expressed thanks to the following linguistic elements: the lexicon (le mercure est un élément chimique), the punctuation (the comma in le Mercure, un fleuve du sud de l'Italie), lexical inclusion (appareil de mesure, implying that appareil de mesure is an appareil), layout (disposition and typography) as used in enumerative structures (la diode à vapeur de mercure est un appareil de mesure, la pile au mercure est un appareil de mesure, etc.).

Depending on the language, the disambiguation pages are more or less structured. For instance, pages in English are very highly structured and contain essentially low-written text (noun-phrases and item lists). The French pages, in contrast, more readily mix the well-written text and the low-written text. So the French pages are particularly relevant for our experiment with a larger variety of ways to formulate hypernym relations.

The semantic resource that we use for building training examples is the BabelNet semantic network. BabelNet is both a multilingual encyclopedic dictionary, with lexicographic and encyclopedic coverage of terms, and a semantic network that connects concepts and named entities in a large network of semantic relations, made up of about 14 million entries. It has been automatically created by linking the encyclopedia Wikipedia to other resources, among which WordNet as source of lexical relations. In BabelNet, missing lexical entries in resource-poor languages have We divided the set of disambiguation pages into two sets: a reference corpus composed of 20 pages, and a training corpus composed of the remaining pages (5904 pages). The two corpora have been pre-processed: (i) the plain text has been extracted from these pages, with the help of a WikiExtractor5 ; (ii) this plain text has been annotated with Part-Of-Speech and lemma (using TreeTagger); and (iii) it has been annotated with terms (including both single words and multiword expressions) corresponding to labels of concepts in BabelNet for the training corpus (resp. with the set of manually annotated relations for the reference corpus).

Features, examples and training models 4.2.1. Features

For each sentence in the training set, we extract a set of features from a window of size n. A sentence in the training text contains two terms tagged T erm1 and T erm2, that result from the annotation with the BabelNet concept labels. The window includes n tokens before T erm1, T erm1, the tokens between T erm1 and T erm2, T erm2, n tokens after T erm2. Only the features of the tokens belonging to this window are processed. We consider three kinds of features: those involving tokens, those involving sentences and those involving sentence windows. Currently, we focus on lexical and grammatical features, and some heuristics inspired by the work of Lin and colleagues [START_REF] Lin | Neural relation extraction with selective attention over instances[END_REF] , as they seem enough to provide good results. In fact, we decided no to use syntactic features because syntactic parsers provide poor results on low-written text. Furthermore, limiting the number of required NLP tools makes the approach easier to reproduce, especially for languages for which such tools are scarce. Table 1 presents the set of selected features.

Learning examples

Given that we only consider sentences that contain at least two terms denoting two concepts in BabelNet, the 1. If two terms (in the same sentence) denote two concepts linked by a hypernym relation in BabelNet, a positive example will be built from the sentence window encompassing these two terms; 2. If no hypernym relation links the two BabelNet concepts whose labels occur in the same sentence, a negative example will be built from the sentence window encompassing these two terms.

Hence, for each sentence of each page of the training corpus, we randomly choose a pair of labeled terms [START_REF] Fauconnier | Discovering Hypernymy Relations using Text Layout (regular paper)[END_REF] T erm1 and T erm2 occurring in that sentence. We then explore the BabelNet hierarchy to check whether T erm1 and T erm2 denote concepts linked by the hypernym relation. We empirically set a maximum length path of 3 levels in the hierarchy [START_REF] Kamel | Exploiter la structure discursive du texte pour valider les relations candidates d'hyperonymie issues de structures énumératives parallèles[END_REF] . We give below an example of a feature vector, with a window size set to 3:

"Lime ou citron vert, le fruit des limettiers : Citrus aurantiifolia et Citrus latifolia"

Matching the BabelNet list of terms lead to annotate the sentence with the terms Lime, citron, citron vert, vert, fruit. Let us consider the pair <Lime, fruit> randomly chosen by the system : Term1=Lime and Term2=fruit. The system thus extracts:

Terme1 ou citron vert, le Terme2 des limettiers :

where tokens corresponding to terms have been replaced with T erm1 and T erm2. Tree Tagger parsing allows to replace the exact form of tokens by their part-of-speech followed by their lemma:

Terme1 KON/ou NOM/citron ADJ/vert PUN/, DET:ART/le Terme2 PRP:det/du NOM/limettier PUN/:

We finally compute distance features: for each token in the window, the feature is a pair of values representing the distance (in number of words) of this token respectively with T erm1 and T erm2. The last three features are the number of tokens between T erm1 and T erm2 (here 5); the number of tokens in the whole sentence (here 16); true or false depending on the presence (or absence) of a verbal form. This feature contributes to discriminate low-written text from well-written text.

(0,6) (-1,5) (-2,4) (-3,3) (-4,2) (-5,1) (-6,0) (-7,-1) (-8,-2) (-9,-3) 5 16 false

Here is the entire feature list for this example :

Terme1 KON/ou NOM/citron ADJ/vert PUN/, DET:ART/le Terme2 PRP:det/du NOM/limettier PUN/: (0,6) (-1,5) (-2,4) (-3,3) (-4,2) (-5,1) (-6,0) (-7,-1) (-8,-2) (-9,-3) 5 16 false This example is a positive one as a hypernym link between "lime" and "fruit" exists in BabelNet.

Training models

From the whole set of We then produced different learning models (according to the supported features and with different sizes of sentence windows). We evaluated them in terms of precision, recall and F-measure, as shown in Table 2. We can observe that the best results were obtained for a window of size 3. This result can be explained on the one hand by the fact that with a window of size 1, we loose contextual information. On the other hand, a window of size 5 does not bring so much given that the corpus sentences are relatively short and low-written. Hence the contextual information obtained for that 5 word window is not discriminating. From these results, we have trained another model using 3 as window size and all the features listed in Table 1. The results are reported in Table 3. Unlike what we expected, the model using only the POS and lemma with window of 3 (model called in the following model POSL) outperforms also the model considering all the features listed in Table 1 and which we refer to as model AllFeatures in the following, in terms of F-measure. This can be explained by the fact that for that kind of corpus, POS and lemma features seem to be discriminant. Nevertheless we decided to evaluate both models on the reference corpus.

Features : POS and

: all features listed in

Evaluation

The evaluation we present in this section aims at showing the performance (in terms of precision and recall) of the two learning models presented in the previous section (model POSL and model AllFeatures), when applied to a corpus with the same characteristics as the ones on which the models were trained. We also compare the results to two baselines, both based on lexico-syntactic patterns largely used in the literature to identify hypernym relations.

This evaluation concerns the reference corpus composed of 20 French disambiguation pages. Two annotators have annotated this corpus in a double blind process: 688 sentences contained 2 BabelNet terms have been annotated as true positives (hypernym relations) and 278 such sentences as true negatives (absence of hypernym relation). After the calculation of the annotation agreement (of 0.8) between annotators, all conflicts have been identified and resolved.

This corpus has been pre-processed as described in section 4.1. From sentences containing the manually annotated relations, we extracted feature vectors and submitted them to the two classifiers model POSL and model AllFeatures.

Baseline

As stated above, two baselines based on lexico-syntactic patterns have been used. A lexico-syntactic pattern is a regular expression composed of words, POS or semantic categories, and symbols aiming at identifying textual segments which match this expression. In the context of relation extraction, the pattern characterizes a set of linguistic forms whose interpretation is relatively stable and corresponds to a semantic relation between terms [START_REF] Rebeyrolle | Repérage automatique de structures linguistiques en corpus : le cas des énoncés définitoires[END_REF] . Patterns are in fact very efficient, in particular in terms of precision, when they are adapted to the corpus. However, since their development is expensive, it is conventional to implement generic patterns such as those of Hearst [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF] . We chose a more complete list of 30 patterns from the work of Jacques and Aussenac [START_REF] Jacques | Variabilit des performances des outils de TAL et genre textuel. Cas des patrons lexico-syntaxiques[END_REF] . This set of patterns is our first baseline (called Baseline 1 in the following).

In a second step, in order to better take into account the specificity of the corpus, we have defined ad-hoc patterns adapted to the low-written parts of the disambiguation pages [START_REF] Ghamnia | Extraction de relations dhyperonymie partir de wikipdia[END_REF] . The set of generic patterns together with the specific patterns is our second baseline [START_REF] Morsey | Dbpedia and the live extraction of structured data from wikipedia[END_REF] (called Baseline 2 in the following).

Results and discussion

Table 4 shows the results of the two baselines and the two classifiers, in terms of precision, recall, F-measure and accuracy. We can observe that the model AllFeatures model gives the best results, with a F-measure equal to that of model POSL but with a better accuracy. As expected, in terms of precision, the generic patterns (Baseline 1) outperform all the other models, in detriment of recall. When using specific patterns (Baseline 2), recall is highly improved. However, they introduce some noise (false positives) and decrease precision. Since the accuracy is substantially the same (except to Baseline 1), another way of looking at these results is given in Table 5, which presents the number of true positive hypernym relations per type of hypernym expression in the text, found by the baselines and by the classifiers. With the classifiers, we are able to identity the most recurrent forms of expression of the hypernym relation, namely those relations expressed with well-written text, as in "T erm1 is a T erm2", those expressed using layout and therefore expressed with low-written text, as in "T erm1, T erm2", and as well as those which can be identified thanks to the head-modifier method. While the learning approach is able to identify a larger variety of expression forms of the relation, outperforming the symbolic approach, the patterns introduced in the Baseline 2 seem to fit well the corpus regularities. The intersection of the results provided by these methods shows that 11 true positive hypernym relations were found both by Baseline 1 and by the two classifiers, while 221 true positive hypernym relations were found both by Baseline 2 and by the two classifiers. From a quantitative point of view, automatic learning identifies more examples than patterns, without any development cost, ensuring a systematic and less empirical approach. From a qualitative point of view, classifiers do not perform as well as patterns when relations are regularly expressed in the same way, as somehow expected, but they can identify more varied forms of relation expressions. For instance, patterns have not the ability to identify relations with head modifiers, while the classifiers are able to do so. The following examples show the expression of hypernym relations in complex sentences that could be correctly identified by the classifiers:

Relations expressed in

(1) <Louis Label, prêtre-missionnaire oblat> and <Louis Label, explorateur du Nouveau-Québec> in the sentence Louis Babel, prêtre-missionnaire oblat et explorateur du Nouveau-Québec (1826-1912).

(2) <fontaine, robinet de cuivre> in the sentence La fontaine a aussi désigné le "vaisseau de cuivre ou de quelque autre métal, où l ′ on garde de l'eau dans les maisons", et encore le robinet de cuivre par où coule l'eau d'une fontaine, ou le vin d ′ un tonneau, ou quelque autre liqueur que ce soit.

In these examples, the relations are expressed within textual units using conjunction, as in example (1), or with T erm1 and T erm2 being relatively far from each other in the sentence, as in example (2). A pattern approach would require the definition of new patterns to fit these cases.

Conclusion and perspectives

This paper has presented a distant supervision learning approach for extracting hypernym relations from a corpus where hypernymy is expressed in a variety of forms. We composed a corpus from Wikipedia disambiguation pages, which are rich in hypernym relations expressed through textual definitions and named entities. We have mainly used lexical and grammatical features. Even with this reduced set, we could observe that the learning approach is able to extract regularities from the corpus. It was able to correctly identify different ways of expressing relations, including a set of those that could be identified by patterns or head-modifiers, for instance.

We have evaluated learning and patterns independently. However their combination seems to be a good strategy. In fact, combining learning with a broad set of patterns, best suited to the corpus, could ensure the best results (at the cost of developing such patterns). In the case of very regular pages with singularities (such as disambiguation pages), the development of ad-hoc patterns is immediate and justified.

As future work, we plan to train a model on the whole set of Wikipedia pages. We also intend to investigate additional features such as semantic, distributional or lay-out features. Moreover, we plan to study how patterns and learning approaches can be better combined.

Fig. 1 .

 1 Fig. 1. Excerpt from the French disambiguation page of the word Mercure

  84169 training examples produced according to the process described above, 4792 examples are labeled as positive, and 79377 are labeled as negative. We randomly took 3000 positive examples and 3000 negative examples. From these 6000 examples, 4000 were used as the set of training examples and 2000 as the test set (with a rate of 50% of positive examples, for both training and test sets).

Table 1 .

 1 construction of training examples leans on the following assumptions: Features set.

	Scope	Features	Signification	Type
	Token	POS	Part Of Speech	string
		lemma	Lemmatized form of token	string
		distT1	Number of tokens between the token and Term1 integer
		distT2	Number of tokens between the token and Term2 integer
	Window	nbWordsWindow	Number of tokens in the window	integer
		distT1T2	Number of tokens between Term1 and Term2	integer
	Sentence nbWordsSentence Number of tokens in the sentence	integer
		presVerb	Presence of a verbal form	boolean

Table 2 .

 2 Results for POS and Lemma features, for different window sizes.

			Lemma
		window size=1 window size=3 window size=5
	Precision	0.67	0.69	0.69
	Recall	0.72	0.78	0.76
	F-measure	0.69	0.73	0.72

Table 1

 1

	and window size=3

Table 3 .

 3 Results for all features of Table1and for a window size = 3.

Table 4 .

 4 Results for the two baselines and the two classifiers on the reference corpus.

		Baseline 1 Baseline 2 Model POSL Model AllFeatures
	Precision	0.96	0.81	0.68	0.71
	Recall	0.04	0.46	0.66	0.63
	F-measure	0.07	0.59	0.67	0.67
	Accuracy	0.31	0.54	0.53	0.55

Table 5 .

 5 Results for the two baselines and the two classifiers for the different ways of expressing the hypernym relation.

			Relations expressed in Relations expressed with Total
		well-written text	low-written text	head-modifiers
	Baseline 1	24	2	0	26
	Baseline 2	23	294	0	317
	Model POSL	22	243	187	452
	Model AllFeature	31	261	139	431

http://www.irit.fr/Sempedia

DBPedia is a crowd-sourced community effort to extract structured information from Wikipedia and make it available on the Linked Open Data

http://opennlp.apache.org/

https://fr.wikipedia.org/wiki/Mercure

http://wiki.apertium.org/wiki/Wikipedia Extractor

In order to obtain reasonable computation time and to provide sets of examples of reasonable size, we do not compute all possible combinations of pairs of terms from a single sentence

A JAPE implementation of these two types of patterns is visible on the site:https://github.com/aghamnia/SemPediaPatterns

Acknowledgement

The Sempedia Project is funded by the French region Occitanie-Mediterranée-Pyrénées from 2015 to 2018. This work also contributed to the SparkinData project funded by the French FUI program from 2015 to 2017.


Gerald Chanques
email: g-chanques@chu-montpellier.fr

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MD J-M Delay

MD Audrey De Jong

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Samir Jaber

Nicolas Molinari

Prof J-Y Lefrant

Prof J-M Constantin

Prof Emmanuel Futier

Prof J-M Constantin

Prof Gérald Chanques

Gerald Chanques

Jean-Michel Constantin

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Jean-Yves Lefrant

Immediate interruption of sedation compared with usual sedation care in critically ill postoperative patients (SOS-Ventilation): a randomised, parallel-group clinical trial

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Introduction

More than 300 million surgical procedures are done worldwide each year. [START_REF] Meara | Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development[END_REF] When organ dysfunction occurs during surgery (eg, haemorrhagic or septic shock, or acute respiratory failure), patients are directly admitted to intensive care units (ICUs) for organ support, including mechanical ventilation and sedation. [START_REF] Futier | A trial of intraoperative lowtidalvolume ventilation in abdominal surgery[END_REF] However, general anaesthesia and sedation in the ICU could distort the perceived severity of a patient's condition because sedatives have complex interactions with organ dys function, reduce respiratory drive and blood pressure, and therefore increase the need for mechanical ventilation and vasopressors. Moreover, an abundant literature, mostly concerning medical ICU patients, has shown that sedation is associated with increased morbidity, resource utilisation, and costs because of delayed ventilator weaning, prolonged ICU and hospital stays, and compli cations (especially delirium). [START_REF] Kress | Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation[END_REF][START_REF] Brook | Effect of a nursingimplemented sedation protocol on the duration of mechanical ventilation[END_REF][START_REF] Ouimet | Incidence, risk factors and consequences of ICU delirium[END_REF][START_REF] Pandharipande | Longterm cognitive impairment after critical illness[END_REF] Avoidance of unnecessary deep or prolonged sedation is now a key objective when caring for critically ill patients. [START_REF] Barr | Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit[END_REF][START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF] Worldwide, best practice recommendations for achieving this goal include a sedation protocol based on the use of clinical sedation scales [START_REF] Brook | Effect of a nursingimplemented sedation protocol on the duration of mechanical ventilation[END_REF] or daily interruption of sedatives, [START_REF] Kress | Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation[END_REF] or both. [START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF] However, such sedation practices are applied in less than a third of ICUs throughout the world, [START_REF] Mehta | Current sedation practices: lessons learned from international surveys[END_REF][START_REF] Gill | Perceived versus actual sedation practices in adult intensive care unit patients receiving mechanical ventilation[END_REF][START_REF] Dodek | Role of organisational structure in implementation of sedation protocols: a comparison of Canadian and French ICUs[END_REF][START_REF] Carrothers | Contextual issues influencing implementation and outcomes associated with an integrated approach to managing pain, agitation, and delirium in adult ICUs[END_REF] making them one of the least applied ICU practice recommendations. [START_REF] Leone | Variable compliance with clinical practice guidelines identified in a 1day audit at 66 French adult intensive care units[END_REF] Thus, according to guidelines, most mechanically ventilated patients are still oversedated. [START_REF] Shehabi | Early intensive care sedation predicts longterm mortality in ventilated critically ill patients[END_REF][START_REF] Shehabi | Sedation depth and longterm mortality in mechanically ventilated critically ill adults: a prospective longitudinal multicentre cohort study[END_REF] A study [START_REF] Balzer | Early deep sedation is associated with decreased inhospital and twoyear followup survival[END_REF] in 1884 mechanically ventilated ICU patients showed that patients admitted to the ICU after unplanned surgery were twice as often oversedated during the first 48 h compared with patients admitted after elective surgery.

Severe pain can lead to specific issues in the management of sedation and analgesia in postoperative ICU patients. Strøm and colleagues [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] showed that a trial of no sedation within 24 h after intubation in a mixed medical and surgical population of ICU patients was associated with an increase in days spent without ventilation and a decrease in days spent in the ICU and hospital, but also with an increased incidence of delirium and antipsychotic requirements. The effect of early interruption of sedatives in postoperative patients remains unknown. Although pain might necessitate high analgesia and sedation requirements, anaesthesia could worsen overall clinical status.

We hypothesise that certain critically ill patients admitted to an ICU following surgery might not actually require sedation and ventilation at all, and that avoidance of continuous sedation as soon as possible would be a feasible strategy associated with improved outcomes. Because of the substantial number of postoperative patients sedated for mechanical ventilation in the ICU in whom avoidance of continuous sedation could be a feasible intervention, we did the Strategy of Optimized SedationVentilation (SOSVentilation) study to evaluate whether immediate cessation of sedation could improve postoperative outcomes compared with usual sedation care.

Methods

Study design

The SOSVentilation study was an investigatorinitiated, multicentre, stratified, parallelgroup clinical trial with a computergenerated allocation sequence and centralised randomisation. In accordance with French law, the study protocol and statistical analysis plan were approved for all centres by the local ethics committee (Comité de Protection des Personnes SudMéditerranée IV, Montpellier, France). The full protocol is provided in the appendix. The trial was done in accordance with the Declaration of Helsinki and was registered on Nov 10, 2011 (ClinicalTrials.gov, number NCT01486121). Three ICU centres participated in the study, with a patient to nurse ratio of 2•5 to 1 and a patient to assistantnurse ratio of 4 to 1 (patient to bedside caregiver ratio of 1•5 to 1). Unlike the randomised trial by Strøm and colleagues, [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] no extra individuals (such as an assistant nurse or a family member) were required to reassure the nonsedated patients specifically for the purpose of the present trial.

Patients

Patients with national healthcare insurance, who were older than 18 years, intubated and mechanically ventilated in volume assistcontrol mode in ICUs less than 24 h after a surgery, for an expected 12 h mechanical ventilation, with at least one organ dysfunction defined by a Sequential Organ Failure Assessment (SOFA) score [START_REF] Ferreira | Serial evaluation of the SOFA score to predict outcome in critically ill patients[END_REF] greater than 1 for any organ, were eligible for participation as soon as they were postoperatively normothermic (body temperature >36°C) without any residual paralysis induced by neuromuscular blocking

Research in context

Evidence before this study We searched MEDLINE and ClinicalTrials.gov from their inception to March 21, 2017. The search term equation was: ((ICU) or (critical care)) and ((sedation) or (sedatives) or (analgesia) or (analgesics) or (pain) or (agitation) or (delirium) or (mechanical ventilation)). Studies were included if they evaluated an intervention concerning sedation practices aimed at decreasing sedation in intensity (ie, level of sedation) or duration, or both, in adult patients aged 18 years or older and admitted to an intensive care unit (ICU). We found one randomised controlled trial assessing the effect of early interruption of sedation in a mixed medical and surgical population of ICU patients within 24 h after intubation. The no-sedation group had a significant increase in days without ventilation and a decrease in days in the ICU and hospital compared with the control group. After completion of our trial, a meta-analysis of six randomised controlled trials of protocolised sedation in medical or mixed medical and surgical ICU patients reported a significant association between protocolised sedation and a reduction in overall mortality and length of stay in ICU and hospital compared with usual care. The quality of the evidence was moderate for all six trials. However, none of these trials exclusively enrolled postoperative surgical ICU patients; also, enrolment occurred after a 24-48 h period of mechanical ventilation. Thus, we identified a need for an interventional study on sedation practices during the early period of mechanical ventilation.

Added value of this study

In this study, sedation was decreased in postoperative, critically ill ICU patients much sooner than in previous studies. Sedation was interrupted as soon as possible after admission to the ICU from the operating theatre. Patients in the intervention group had a median time of less than 2 h of sedation. Unnecessary deep and prolonged sedation in the control group (where median sedation time was 33 h) was also avoided to prevent iatrogeny. The effect of this immediate interruption of sedation was a significantly shorter median time to successful extubation in the intervention group than in the control group (8 h vs 50 h). Moreover, this study is, to our knowledge, the first randomised controlled trial evaluating a sedation intervention that showed a positive effect on delirium. The findings of our study reinforce the rationale for interrupting sedation as early as possible in ICU patients.

Implications of all the available evidence

Given the clinical and economic burden of critical illness, postoperative morbidity, and the substantial number of patients who could benefit from this strategy, sedation should be immediately interrupted in postoperative patients admitted to an ICU. Further studies should be done to investigate whether such an immediate cessation of sedation would have similar effects in medical ICU and postoperative ICU patients with a more severe acute respiratory failure at time of admission or patients undergoing surgery other than abdominal surgery.

agents potentially used for anaesthesia, as clinically assessed by the head lift test or with a monitoring device according to local practices.

Patients who had been admitted to the hospital ICU for more than 7 days before surgery, had brain injury, had severe acute respiratory distress syndrome (ARDS, as previously defined in the ACURASYS trial [START_REF] Papazian | Neuromuscular blockers in early acute respiratory distress syndrome[END_REF] by a ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen of less than 150), had a history of drug abuse, had withdrawal of care, were under guardianship, were pregnant, were enrolled in another trial evaluating sedation or ventilation, or had a surgical contraindication to discontinuing sedation (ie, uncontrolled bleeding, surgical reinter vention planned within 24 h, or open abdominal wall) were excluded. The trial was interrupted if patients or their proxies declined participation or if brain injury occurred after enrolment.

Taking into account the fact that patients would be sedated following ICU admission, and that certain admissions would occur after an unplanned surgery under emergency conditions, complete adherence to patient consent procedures was deemed impossible before surgery or following ICU admission. In accordance with French law and with the approval of the ethics committee in favour of the research objective, a consent dispensation for emergency situations was enabled to minimise the time between ICU admission and randomisation as much as possible. Written consent to continue the research and analyse the data was obtained from the patient or their proxies as soon as possible.

Randomisation

Patients were screened and underwent randomisation between Dec 2, 2011, and Feb 27, 2014. Patients were randomly assigned (1:1 ratio) by stratified randomisation with minimisation via a restricted web platform. Randomisation was stratified according to centre, the patient's age (<60 years or ≥60 years), and illness severity assessed by the Simplified Acute Physiological Score (SAPS) II [START_REF] Legall | New Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF] (SAPS II score <38 or ≥38). These cutoffs were determined on the basis of the mean scores observed in our ICU population. [START_REF] Chanques | Sedation protocols for intubated patients and non invasive ventilation: additional concepts for a noniatrogenic intensive care[END_REF] The SAPS II score used for stratification randomisation took into account the worst value available up to the previous 24 h from randomisation.

Procedures

Patients were assigned to receive either continuous sedation for tolerance of assistcontrol ventilation accord ing to recommended guidelines (control group) or an immediate interruption of sedation (intervention group). In the intervention group, when anxiety, agitation, pain, discomfort, polypnoea, or patient-ventilator asyn chrony persisted after management optimisation accord ing to established protocols (appendix pp 5-9), con tinuous sedation was used for 6 h. If more than two periods of sedation were required within 24 h, continuous sedation was prolonged until the next day.

In both groups, sedation was standardised according to recommended guidelines [START_REF] Barr | Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit[END_REF][START_REF] Sauder | Sedation and analgesia in intensive care (with the exception of newborn babies)[END_REF] to minimise the risk of oversedation. A previously published sedation analgesia protocol [START_REF] Dodek | Role of organisational structure in implementation of sedation protocols: a comparison of Canadian and French ICUs[END_REF] was used by bedside nurses every 4 h and sedation levels and pain intensity were assessed with the Richmond Agitation Sedation Scale (RASS) [START_REF] Sessler | The Richmond AgitationSedation Scale: validity and reliability in adult intensive care unit patients[END_REF][START_REF] Ely | Monitoring sedation status over time in ICU patients: reliability and validity of the Richmond AgitationSedation Scale (RASS)[END_REF] and the Behavioral Pain Scale (BPS). [START_REF] Payen | Assessing pain in critically ill sedated patients by using a behavioral pain scale[END_REF] Sedation was primarily targeted at a light or moderate level (RASS between -1 and -3). Sedatives were interrupted daily every morning according to criteria selected by French intensive care societies (see the protocol in the appendix p 5). [START_REF] Sauder | Sedation and analgesia in intensive care (with the exception of newborn babies)[END_REF] After interruption of sedation, agitation was again assessed every 4-8 h until discharge from the ICU by the RASS in all patients (sedated and nonsedated, intubated and nonintubated). Pain was assessed either by the BPS in patients unable to communicate or by the visually enlarged 0-10 Numeric Rating Scale (NRS) [START_REF] Chanques | The measurement of pain in intensive care unit: comparison of 5 selfreport intensity scales[END_REF] in those able to communicate. Diagnosis and therapeutic management of pain and agitation were standardised according to published protocols. [START_REF] Chanques | Impact of systematic evaluation of pain and agitation in an intensive care unit[END_REF][START_REF] De Jong | Decreasing severe pain and serious adverse events while moving intensive care unit patients: a prospective interventional study (the NURSEDO project)[END_REF] Major opioid infusions (ie, sufentanil or remifentanil) were stopped at the same time as sedatives. Postoperative analgesia was provided by use of a multimodal strategy with paracetamol, nefopam, and tramadol (appendix p 9). Major opioids were reintroduced if multimodal analgesia was insufficient for treatment of basal pain, or for prevention and treatment of procedural pain. [START_REF] Chanques | Impact of systematic evaluation of pain and agitation in an intensive care unit[END_REF][START_REF] De Jong | Decreasing severe pain and serious adverse events while moving intensive care unit patients: a prospective interventional study (the NURSEDO project)[END_REF] When severe ARDS occurred after randomisation, deep sedation was reintroduced at the same time as a neuromuscular blocking agent (cisa tracurium) for a maximum of 48 h, according to the ACURASYS sedation protocol. [START_REF] Papazian | Neuromuscular blockers in early acute respiratory distress syndrome[END_REF] Patients who developed severe ARDS were not excluded from the trial or the analysis.

Ventilators were set according to a lung protective ventilation strategy in both groups. [START_REF] Acute | Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome[END_REF] Pressure support ventilation, a mode allowing for spontaneous breathing and less asynchrony, [START_REF] Chanques | Impact of ventilator adjustment and sedationanalgesia practices on severe asynchrony in patients ventilated in assistcontrol mode[END_REF] was used after interruption of sedation. Sedation interruption was coupled with a spontaneous breathing trial [START_REF] Girard | Efficacy and safety of a paired sedation and ventilator weaning protocol for mechanically ventilated patients in intensive care (Awakening and Breathing Controlled trial): a randomised controlled trial[END_REF] on at least a daily basis. Extubation was implemented in accordance with standardised criteria, as recommended [START_REF] Boles | Weaning from mechanical ventilation[END_REF] (appendix p 5).

Outcomes

The primary outcome measure was time to successful extubation, defined as the time from randomisation to the time of extubation (or tracheotomy mask) for at least 48 h, as previously defined [START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF] (the time of extubation corresponds to the beginning of a 48 h tubefree period). This outcome was centrally assessed. Secondary outcomes calculated on day 28 were the number of ventilatorfree days (ie, days alive and without invasive mechanical ventilation between day 1 and day 28), the duration of ICU and hospital stays, and mortality. Secondary outcome reporting included the following healthcarerelated complications during the ICU stay: coma as defined by a RASS of -4 or -5; delirium as defined by the Confusion Assessment Method for the Intensive Care Unit (CAMICU); [START_REF] Chanques | The CAMICU has now a French "official" version. The translation process of the 2014 updated Complete Training Manual of the Confusion Assessment Method for the Intensive Care Unit in French (CAMICU.fr)[END_REF] pain as defined by a BPS score of more than 3 (or a selfreported 0-10 NRS >3 in patients able to communicate), evaluated by the research team every morning; ileus; pressure ulcer; selfremoval of medical devices; surgical reintervention; healthcare associated infections (for definitions, see the appendix p 10), and healthcare resource utilisation (type, duration, and dose of sedatives and analgesics, and vasopressor (norepinephrine) use within the previous 24 h, with high dose norepinephrine defined as >0•1 µg/kg per min according to the SOFA score). The characteristics of mechanical ventilation, including the use of noninvasive ventilation, were assessed by the research team every morning for the previous 24 h. Longterm outcomes including mortality, posttraumatic stress disorder, anxiety, depression, and quality of life at 3 months and at 12 months were also assessed as prespecified secondary outcomes. [START_REF] Barr | Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit[END_REF]

Statistical analysis

We calculated that a sample size of 140 patients would pro vide a power of 80% to detect an absolute betweengroup difference of 72 h with an SD of 140 h in the primary outcome at a twosided alpha level of 0•05.

All analyses were done on data from the modified intentiontotreat population, which included all patients who underwent randomisation except for those who were excluded after randomisation. Group medians for the primary outcome were compared by use of the MannWhitney U test (the day of extubation was considered as the day of death for patients who died while still intubated). Hazard ratios (HRs) with 95% CIs were calculated by use of multivariate Cox regression to take into account the censored primary outcome for deceased patients. Stepwise selection was used to determine the final Cox regression model (p<0•10 to enter the model, p<0•05 to remain in the final model, in addition to randomisation stratification parameters). A competing risk model for the competing risk of death was used as a sensitivity analysis. A secondary analysis of the primary outcome measure involving a bootstrapped t test (frailtypack in R) was also done to support the findings of our original analysis.

For secondary outcomes, continuous variables were compared with the unpaired t test or the MannWhitney U test; categorical variables were compared with the χ² test or Fisher's exact test, as appropriate (univariate analysis).

For the timetoextubation analysis, the event occurred when a patient was extubated within 28 days from randomisation and remained extubated for more than 48 h. Patients who died before extubation were censored at death. The timetoevent curves were calculated for hospital discharge by use of the same method. All analyses were done with R statistical software, version 3.0.1, and SAS, version 9.3. A twosided p value of less than 0•05 was considered to indicate statistical significance.

This study is registered with ClinicalTrials.gov, number NCT01486121.

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results

From Dec 2, 2011, to Feb 27, 2014, 1614 ICU patients were assessed for eligibility. Among the 186 eligible patients, 137 were included in the intentiontotreat analysis and were followed up for 28 days (figure 1). Data for the primary outcome measure were available for all patients. Of the 137 patients, 68 were randomly assigned to the control group and 69 to the intervention group. Following inclusion, baseline characteristics were similar in both groups (table 1; appendix pp 11-15). All patients were admitted to ICU after abdominal surgery, primarily for septic shock. Median time between randomisation and successful extubation (the primary outcome) was significantly lower in the intervention group than in the control group (table 2; figure 2). A multivariate analysis of variables associated with the primary outcome included variables for which a p value was less than 0•10 after univariate analysis (SOFA score, history of hypertension, opioid treatment, Knauss chronic health status, reason for unplanned surgery, procedural severity scores, and variables at ICU admission: serum lactate, vasopressors, midazolam, propofol, propofol dose, RASS, BPS, and plateau airway pressure) and stratification variables (centre, age, SAPS II). Results of the univariate and multivariate analysis are shown in the appendix (pp 16-19). There were three independent variables remaining in the final model: intervention, SAPS II, and plateau airway pressure at admission. The intervention was significantly associated with reduced time to successful extubation (adjusted HR 5•2; 95% CI 3•1-8•8, p<0•0001; table 2). The HR for the competing risk of death was 3•5 (95% CI 2•2-5•6, p<0•0001; sensitivity analysis), and the bootstrapped modelling of the adjusted HR was 6•0 (3•3-13•0; p<0•0001; table 2).

The logrank test for betweengroup differences applied to the KaplanMeier estimates for the primary outcome yielded a p value of less than 0•0001 in favour of the intervention group (appendix p 27).

Fewer patients in the intervention group had a coma or delirium compared with the control group (table 2). There were significantly more deliriumfree days in the intervention group than in the control group (median

Respiratory subscore 2 (1-3) 2 (2-3) Renal subscore 1 (0-2) 1 (0-2)
Liver subscore 0 (0-2) 0 (0-2)

Coagulation subscore 0 (0-1) 0 (0-1)

Neurological subscore 0 (0-0) 0 (0-0) Use of vasopressors (norepinephrine) 53 (78%) 58 (84%) Serum lactate, mmol/L 3 (2-4) 3 (2-6)
Data are n (%) or median (IQR), unless otherwise stated. The sum of percentages is different from 100% because there could be several reasons for ICU admission. ICU=intensive care unit. *Serum creatinine was available before surgery in 51 patients in the control group and in 54 in the intervention group. †Procedural Severity Scores were calculated according to Dalton and colleagues' method. [START_REF] Dalton | Development and validation of a risk quantification index for 30day postoperative mortality and morbidity in noncardiac surgical patients[END_REF] ‡Intraoperative fluid administration was available in 55 patients in each group, calculated according to Shin and colleagues' method. [START_REF] Shin | Effects of intraoperative fluid management on postoperative outcomes: a hospital registry study[END_REF] §The Simplified Acute Physiology Score (SAPS) II is based on 17 variables; scores range from 0 to 163, with increasing scores indicating increased disease severity. [START_REF] Legall | New Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF] ¶The Sequential Organ Failure Assessment (SOFA) score includes subscores ranging from 0 to 4 for each of six components (circulation, lungs, kidneys, liver, coagulation, and CNS). The SOFA neurological score was calculated on the basis of a clinical assessment before anaesthesia induction. Aggregated scores range from 0 to 20, with higher scores indicating more severe organ failure. 18 There were no significant differences between groups for the other complications observed in the ICU (pain, selfremoval of medical devices, ileus, pressure ulcers, healthcareassociated infections, and surgical reinterventions).

The use of prophylactic or curative postoperative non invasive ventilation did not differ significantly between groups (33 [48%] of 69 in the intervention group vs 36 [53%] of 68 in the control group; absolute difference -5%, p=0•55).

Other healthcare resource utilisation was significantly reduced in the intervention group, with an increased number of ventilatorfree days, fewer days on highdose vasopressors (norepinephrine), and a higher probability of being discharged at day 28 (table 2; figure 3). Day 28 mortality did not differ signifi cantly between the two groups (table 2). No significant difference between the two groups was observed for mortality, posttraumatic stress disorder, anxiety, depression, and quality of life at 3 months and 12 months (appendix pp 25, 26).

After randomisation, continuous sedation was stopped according to the protocol after a median time of 15 min in the intervention group and 33 h in the control group (table 3). Sedation was resumed in less than 30% of patients in both groups, mainly due to patient-ventilator asynchrony (62%) or surgical reintervention (26%), without any significant difference between groups. Seven patients in the control group had a protocol deviation with an immediate interruption of sedation, and one patient in the intervention group had a longer duration of sedation than expected. The appendix (pp 20, 21) describes the drugs used for sedation analgesia after randomisation in both groups. As expected, according to the research protocol, sedatives and major opioids were used significantly more often in the control group than in the intervention group, whereas minor opioids (eg, tramadol) were used signifi cantly more often in the intervention group. There were no significant differences between groups in the use of other drugs (eg, neuromuscularblocking agents, anti psychotics, and anxiolytics).

Discussion

In this multicentre, randomised controlled trial of critically ill postoperative ICU patients after abdominal surgery, immediate interruption of sedation and early use of spontaneous ventilation decreased the time to extubation compared with usual sedation care. Immediate inter ruption of sedation also led to significant decreases in coma, delirium, and highdose vasopressor use. Patients who received the intervention had a signifi cantly higher number of ventilatorfree days and a higher probability of being discharged at day 28 than did patients who received usual care.

Times to extubation reported in both groups in our study are much lower than those reported in previous randomised controlled trials evaluating sedation practices. [START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF][START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF][START_REF] Minhas | Effect of protocolized sedation on clinical outcomes in mechanically ventilated intensive care unit patients: a systematic review and metaanalysis of randomized controlled trials[END_REF] There are two main reasons that might help explain these differences: the duration of sedation before enrolment was over 48 h in most previous studies, whereas in our study enrolment occurred immediately after ICU admission (following a surgical Most patients in our study were admitted directly to the ICU for septic shock after abdominal surgery. Intraabdominal infection is the second leading cause of ICU admission (after complicated pneumonia), accounting for almost 10% of all ICU patients, [START_REF] Barie | Longitudinal outcomes of intraabdominal infection complicated by critical illness[END_REF][START_REF] De Waele | Abdominal infections in the intensive care unit: characteristics, treatment and determinants of outcome[END_REF] and almost 20% of all patients with infections. [START_REF] De Waele | Abdominal infections in the intensive care unit: characteristics, treatment and determinants of outcome[END_REF] It is the most common cause of infection (70%) in septic patients admitted to a surgical ICU. [START_REF] Moore | The epidemiology of sepsis in general surgery patients[END_REF] In patients with septic shock, sedation is often required to manage invasive treatments and to enable tolerance to mechanical ventilation, improve gas exchange, control agitation and pain, and therefore improve tissue oxygenation. [START_REF] Dellinger | Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012[END_REF] A fundamental reason for our trial was to ascertain whether the sustained use of sedatives during recovery after surgery might be unnecessary and lead to increased iatrogenic morbidity and increased resource utilisation. Our hypothesis was that in this specific population of critically ill patients in the postoperative period of abdominal surgery, haemodynamic instability could be, in part, the consequence of the vasodilator sideeffects of anaesthesia drugs. After transfer to the ICU, anaesthesia is continued to some extent by intravenous sedatives (continuous sedation), until the patient recovers from hypothermia and neuromuscular blockade. After this point, the benefits of sedation are unclear. The vasodilator effects of sedation, especially propofol, might interfere with the course of disease itself (sepsis), thus artificially worsening the clinical condition. [START_REF] Singer | The third international consensus definitions for sepsis and septic shock (Sepsis3)[END_REF][START_REF] Shankarhari | Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (Sepsis3)[END_REF][START_REF] Seymour | Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis3)[END_REF] In support of this hypothesis, we observed a significant reduction in the duration of highdose vasopressor use in the intervention group, although illness severity (as assessed by SAPS II and SOFA scores) and serum lactate did not differ significantly between the groups at enrolment (appendix p 13). This study showed that, to test related effects on the overall clinical picture for a given patient, sedation should be stopped as soon as possible, immediately after the patient's transfer from the operating room. We showed that resumption of sedation was unnecessary in more than 70% of these patients. The 2012 International Guidelines for Management of Severe Sepsis and Septic Shock (Surviving Sepsis Campaign) [START_REF] Dellinger | Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012[END_REF] recommended minimisation of sedation through appropriate protocols, lighter sedation targets, or daily interruption of sedatives. Severe pain was defined by a BPS greater than 5 or an NRS score greater than 6. Moderate pain was defined by a BPS of 4-5 or an NRS of 4-6, according to usual definitions. [START_REF] De Jong | Decreasing severe pain and serious adverse events while moving intensive care unit patients: a prospective interventional study (the NURSEDO project)[END_REF] Missing data on pain assessment correspond to one patient in the control group. ¶Comparisons between groups were made by use of the log-rank test, with the variable being truncated at day 28.

Table 2: Primary and secondary outcomes according to study group

Our study in postoperative patients with septic shock suggests that sedation should in fact be completely stopped as soon as possible after ICU admission. In this population of surgical ICU patients, resumption of sedation was often determined by the need for surgical reintervention (30%), but the main reason was patient-ventilator asynchrony. Sedation is intertwined with mechanical ventilation, as has been previously shown for the development of ventilatorinduced diaphragmatic dysfunction: for greater control of mechanical ventilation, more sedatives are required and the risk of ventilatorinduced diaphragmatic dysfunction is increased. [START_REF] Jaber | Rapidly progressive diaphragmatic weakness and injury during mechanical ventilation in humans[END_REF][START_REF] Jung | Adaptive support ventilation prevents ventilatorinduced diaphragmatic dysfunction in piglet: an in vivo and in vitro study[END_REF][START_REF] Levine | Rapid disuse atrophy of diaphragm fibers in mechanically ventilated humans[END_REF] Consequently, while attempting to reduce the use of sedatives in critically ill patients, it also seems paramount that mechanical ventilation be carefully adapted. [START_REF] Chanques | Impact of ventilator adjustment and sedationanalgesia practices on severe asynchrony in patients ventilated in assistcontrol mode[END_REF][START_REF] Jaber | Effect of noninvasive ventilation on tracheal reintubation among patients with hypoxemic respiratory failure following abdominal surgery: a randomized clinical trial[END_REF] Another feature of the present study is the protocolisation of pain, agitation, and delirium management in both groups. This protocol is now recommended as the socalled ABCDEF approach. [START_REF] Ely | The ABCDEF bundle: science and philosophy of how ICU liberation serves patients and families[END_REF] Our study represents a step forward by showing that an approach involving reduced use of sedatives and major opioids (which are frequently associated with sideeffects such as decreased respiratory drive, delayed ventilator weaning, and increased risk of coma and delirium) should be integrated into a comprehensive strategy for the systematic assessment and management of pain, agitation, and delirium (which should be further associated with early mobilisation).

Strengths of this trial include the methods used to mini mise bias (centralised randomisation, complete followup, and intentiontotreat analyses). The trial protocol was pragmatic, with recommended practices applied by intensive care staff, including nurses and physicians, by use of validated and reliable instruments for sedation, pain, and delirium assessment, thus making it easy to replicate this study. We measured the time between randomisation and the first interruption of sedatives precisely in both groups. The first nosedation trial by Strøm and colleagues [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] interrupted sedation within 24 h after intubation but the time between enrolment and interruption of sedation was not clearly reported. [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] Patients were enrolled in our study a few hours after admission to the ICU, compared with 2 days after admission in most previous studies that have investigated the effect of different sedation strategies. [START_REF] Kress | Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation[END_REF][START_REF] Barr | Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit[END_REF][START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF][START_REF] Girard | Efficacy and safety of a paired sedation and ventilator weaning protocol for mechanically ventilated patients in intensive care (Awakening and Breathing Controlled trial): a randomised controlled trial[END_REF][START_REF] Minhas | Effect of protocolized sedation on clinical outcomes in mechanically ventilated intensive care unit patients: a systematic review and metaanalysis of randomized controlled trials[END_REF] These previous studies were done mostly in medical ICU patients. However, our trial, together with the trial by Strøm and colleagues, raises the question of whether or not sedation could have been stopped earlier, and whether starting an intervention aimed at minimising sedation within 2 days of admission to the ICU would improve outcomes or not. Although sedation was suspected to be associated with delirium in previous observational studies, [START_REF] Ouimet | Incidence, risk factors and consequences of ICU delirium[END_REF][START_REF] Pandharipande | Longterm cognitive impairment after critical illness[END_REF][START_REF] Ely | Apolipoprotein E4 polymorphism as a genetic predisposition to delirium in critically ill patients[END_REF][START_REF] Jaber | A prospective study of agitation in a medicalsurgical ICU: incidence, risk factors, and outcomes[END_REF] our study is the first interventional study that shows a positive effect of a nosedation strategy in decreasing the incidence of delirium, contrary to the findings of Strøm and colleagues. [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] This effect might be explained by the different methods used to measure delirium or the different protocols for manage ment of pain and agitation. For example, in our trial we prioritised multimodal analgesia, restricting the use of morphine, whereas in the trial by Strøm and colleagues boluses of 2•5-5•0 mg were used "as needed".

A limitation of our study is that the durations of mechanical ventilation were shorter than expected compared with previous historical observational data on Data are n (%) or median (IQR), unless otherwise stated. RASS=Richmond Agitation Sedation Scale. *RASS level during sedation was calculated among the 68 patients in the control group, during all sedation periods except when sedation was deepened during withdrawal of care. In the intervention group, the RASS level during sedation was calculated in the 18 patients for whom sedation was resumed after the initial interruption that was made upon enrolment according to the study protocol. In the intervention group, the two patients for whom sedation was only resumed during withdrawal of care were not included in the calculation of RASS level during sedation. †In the control group, a protocol deviation was declared if sedation was stopped before the daily interruption of sedation planned every morning. In the intervention group, a protocol deviation was declared if sedation was resumed for more than 6 h in situations other than those allowed by the protocol (ie, development of a severe acute respiratory distress syndrome defined as a ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen of less than 150 with a positive end-expiratory pressure of 5 cm or more of water, or the necessity to resume sedation for more than two 6 h periods within 24 h).

Table 3: Management of continuous sedation after randomisation, according to study group which our hypothesis was based, 21 leading to possible underpowering with a difference in time to extubation between groups of much less than the expected 72 h. This difference could be explained by a positive effect generated by the research protocols and the fact that clinicians were aware that their practices were being monitored (ie, the Hawthorne effect, where a positive psychological effect in team management can be observed in both groups under investigation, with workers being motivated to improve their skills and efficiency). [START_REF] Wickstrom | The "Hawthorne effect"-what did the original Hawthorne studies actually show?[END_REF] Sedation was thus probably reduced in both groups because of stricter application of recom mended practices. Daily interruption of the sedatives in both groups was based on criteria selected by French intensive care societies (see the protocol in the appendix). [START_REF] Sauder | Sedation and analgesia in intensive care (with the exception of newborn babies)[END_REF] These criteria were more restrictive than those of previous studies investigating daily inter ruption of sedation. [START_REF] Kress | Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation[END_REF][START_REF] Mehta | Daily sedation interruption in mechanically ventilated critically ill patients cared for with a sedation protocol: a randomized controlled trial[END_REF] Another limitation of our study is that masking was not possible. Possible biases inherent to such trials investigating sedation strategies aimed at decreasing the duration of mechanical ventilation included possible delays in interruption of sedation or extubation of patients in the control group. However, crossover practices appeared to occur in the reverse direction after randomisation, from the intervention group to the control group: sedation was interrupted immediately in 10% of patients in the control group. To minimise iatrogeny in the control group, we also paid strict attention to the sedation protocol. [START_REF] Brook | Effect of a nursingimplemented sedation protocol on the duration of mechanical ventilation[END_REF] The RASS level was targeted between -1 and -3 to avoid deep sedation in the control group. The duration of venti lation was much lower in the control group than in previous studies on sedation protocols, [START_REF] Brook | Effect of a nursingimplemented sedation protocol on the duration of mechanical ventilation[END_REF][START_REF] Minhas | Effect of protocolized sedation on clinical outcomes in mechanically ventilated intensive care unit patients: a systematic review and metaanalysis of randomized controlled trials[END_REF] as well as in Strøm and colleagues' study. [START_REF] Strøm | A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial[END_REF] This difference suggests that the control group received a high standard of care. However, this difference could also be explained by the char acteristics of our study population, which was restricted to postoperative patients, mostly with septic shock but without severe acute respiratory failure at baseline. This description accounts for 75% (140 of 186) of postoperative adult patients admitted to our ICUs with organ dysfunction. In these patients, sedation could be interrupted earlier than in other ICU populations. Further studies are needed to measure the feasibility of our strategy in medical ICU patients and in patients with more severe acute respiratory failure, as well as in patients undergoing surgery other than abdominal surgery. Further studies are also needed in a large number of ICUs with different patienttonurse ratios and different organisational cultures and skillsets re garding the management of sedationventilation and anaesthesia practices, including monitoring of neuro muscular blockade to avoid any residual paral ysis. [START_REF] Grossesundrup | Intermediate acting nondepolarizing neuromuscular blocking agents and risk of postoperative respiratory complications: prospective propensity score matched cohort study[END_REF] However, a patient to caregiver ratio of 1:1 should probably be recommended for an intub ated patient at the early phase of the postoperative period, to assure careful manage ment of pain, agitation, patient-ventilator asynchrony, monitoring of neuromuscular blockade, body tempera ture, and shivering (supported by specific protocols), as well as regular education and training. All these practices were implemented in our ICUs, but monitoring for residual neuromuscular blockade could have been improved in some patients, which is another limitation of this study. Finally, our trial was not powered to assess secondary outcomes since the study population was small. Additionally, in a small population of selected patients we could not rule out possible safety issues, particularly rare safety events. In summary, our trial provides evidence that a strategy of avoiding continuous sedation as early as possible, in the absence of residual neuromuscular blockade and hypothermia, compared with usual sedation care, resulted in improvements in several important clinical outcomes in critically ill postoperative patients. Given the clinical and economic burden of critical illness, post operative morbidity, and the substantial number of patients who could benefit from this strategy, increased attention should be given to prevention of postoperative iatrogenic injury potentially induced by unnecessary sedation and mechanical ventilation. Future studies should investigate whether immediate interruption of sedation has similar effects in other populations, such as medical ICU patients and patients with more severe acute respiratory failure. This approach would avoid the unncessary 1-2 days of sedation (a high risk period for oversedation) observed before enrolment in previous studies done in this setting. [START_REF] Shehabi | Early intensive care sedation predicts longterm mortality in ventilated critically ill patients[END_REF][START_REF] Shehabi | Sedation depth and longterm mortality in mechanically ventilated critically ill adults: a prospective longitudinal multicentre cohort study[END_REF][START_REF] Balzer | Early deep sedation is associated with decreased inhospital and twoyear followup survival[END_REF] Contributors GC and SJ were on the trial management committee. GC, SJ, JYL, JMC, and GM were on the scientific committee. AP was the Trial Monitoring and Research Coordinator. NM was responsible for statistical and data coordination. GC, SJ, and BJ were on the writing committee. The following investigators at participating sites were responsible for screening of eligible patients and obtaining patients' and families' consent to participate, as well as inclusion, randomisation, and implementation of the research protocol: MCo, JC, BJ, AdJ, FB, MCi, JMD, GC, and SJ at Saint Eloi Hospital (Montpellier, France); CR, LM, and JYL at Caremeau Hospital (Nîmes, France); and JMC and EF at Estaing Hospital (ClermontFerrand, France).

Declaration of interests

JMC reports grants, personal fees, and nonfinancial support from Baxter, during the conduct of the study. LM reports personal fees from Philips Echo, General Electric Echo, Fresenius Kiabi, and Fresenius Medical Care, outside of the submitted work. EF reports personal fees from Baxter, Fresenius Kabi, and General Electric Healthcare; personal fees and nonfinancial support from Edwards, nonfinancial support from Drager, and travel reimbursement from without whose assistance the SOSVentilation study could not have been completed. We also thank Carey M Suehs for her help with English corrections.

Figure 1 :

 1 Figure 1: Trial profile Technical reasons preventing enrolment were related to a high workload in the intensive care unit (ICU). ARDS=acute respiratory distress syndrome.

Figure 2 :

 2 Figure 2: Primary outcome measure, according to study group Median time between randomisation and successful extubation (the primary outcome) was significantly lower in the intervention group than in the control group: 8 h (IQR 4-36) versus 50 h (29-93); mean times were 50 h (SD 13) versus 89 h (14), W=966•5 (Mann-Whitney's test), p<0•0001. For the box and whisker plots, the horizontal double bar indicates the median, the upper and lower limits of the boxes the IQR, and the ends of the whiskers the 95% CI. The diamonds indicate the means with their bars indicating the standard error.

Figure 3 :

 3 Figure 3: Kaplan-Meier estimates for the probability of being discharged from the hospital at day 28, according to study group Deceased patients were censored at death. The p value (with p<0•05 indicating statistical significance) was identified by the log-rank test for a between-group difference in the probability of discharge. HR=hazard ratio.

  Fisher & Paykel, outside of the submitted work. SJ reports personal fees from Drager, Fisher & Paykel, Hamilton, Xenios, and Baxter, outside of the submitted work. All other authors declare no competing interests. www.thelancet.com/respiratory Published online September 18, 2017 http://dx.doi.org/10.1016/S2213-2600(17)30304-1

Table 1 : Baseline characteristics according to study group 26

 1 days [IQR 24-27] vs median 28 days[26-28], p=0•002).

  Data are n (%) or median (IQR), unless otherwise stated. ICU=intensive care unit. NA=not applicable. *Group difference refers to the intervention group value minus control group value: absolute difference (%) or Hodges-Lehmann median difference. †Coma was defined by a Richmond Agitation Sedation Scale (RASS) of -4 or -5, assessed by the research team every morning. ‡Delirium was defined by a positive score on the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), assessed by the research team every morning. Missing data on delirium assessment correspond to one patient in the control group and four in the intervention group. §The research team assessed pain intensity every morning at rest by use of the Behavioral Pain Scale (BPS) in patients unable to communicate, or by use of a visually enlarged 0-10 Numeric Rating Scale (NRS) in patients able to communicate.

		27 (17-28)	23 (14-27)	-2•0 (-3•9 to -0•1)	0•01 ¶
	Mortality
	ICU mortality	14 (21%)	13 (19%)	-2 (-17 to 13)	0•97
	Overall mortality	17 (25%)	17 (25%)	-0 (-16 to 16)	0•99

www.thelancet.com/respiratory Published online September 18, 2017 http://dx.doi.org/10.1016/S2213-2600(17)30304-1

www.thelancet.com/respiratory Published online September 18, 2017 http://dx.doi.org/10.1016/S2213-2600(17)30304-1

Acknowledgments

This study was supported by a public grant from Délégation à la Recherche Clinique et à l'Innovation du Groupement de Coopération Sanitaire de la Mission d'Enseignement, de Recherche, de Référence et d'Innovation (DRCIGCSMERRI) de MontpellierNîmes. We thank all patients who participated in the study, families who gave their consent for participation, and the clinical and research staff at all trial sites,


Mohammed H Modi
email: modimh@rrcat.gov.in

Mangalika Sinha

Aniruddha Bose

Amol Singh

Philippe Jonnard

Depth analysis of Al/ZrC interfaces using SIMS and x-ray reflectivity

The Al/ZrC/Al/W multilayer structure is suitable for waveguide applications in the hard x-ray range in order to confine the wave field in a nanometer-thick layer. Intermixing of Al at the interfaces is a serious problem to achieve the expected performances from an experimentally grown multilayer. In the present study, our aim is to investigate the effect of a capping layer on the ZrC/Al interfaces in an C/Al/ZrC/Al/W waveguide structure. We use time of flight secondary ion mass spectrometry (ToF-SIMS) and soft x-ray reflectivity (SXR) to study the x-ray waveguide structure. Structural parameters of the stack, density, thickness and roughness of the layers, are determined through fitting of SXR data. SIMS results indicate that the Al diffusion towards the top of the stack is responsible of the formation of wide and asymmetric interfaces in the waveguide structure.

Introduction

X-ray waveguides are a modern concept to generate nano-size x-ray beams required for the study of novel properties of nano-materials. X-ray waveguides can also be used to enhance the spatial coherence of x-ray beams and therefore can be very useful in x-ray holography and coherent diffractive imaging experiments. Waveguides are essentially non-dispersive optical devices suited to a wide range of photon energies and bandpass. The limit of full coherence can be reached with proper design and with a suitable choice of the guiding and cladding materials.

X-ray waveguide is analogous to optical fibre where the beam is guided through a core sandwiched between two cladding materials.

In x-ray waveguides, the electric field of the incident photon beam is confined inside a layer through the generation of x-ray standing waves. The guiding layer is generally made of a light material surrounded by two layers made of heavy materials. In the literature very few attempts have been made to understand the role of materials combination, film roughness, interface width, etc. on the performances of waveguides. Simulation shows that the Al/ZrC/Al/W structure can serve as a good combination for waveguide applications in hard x-ray region. Indeed, Figure 1 shows the depth and angular distributions of the electric field of 10.25 keV xrays inside this structure. It is evident that the x-rays can be confined in the Al guiding layer when the glancing angle is close to 0.23°. In order to experimentally realize such high quality confinement one needs to make devices with ultra-smooth interfaces and no interdiffusion layer.

The main challenge in fabrication of such devices is to prevent intermixing between the guiding and cladding layers.

Grazing incidence x-ray reflectivity (GIXR) using Cu K α (0.154 nm) radiation is a powerful tool to probe thickness, roughness and density of thin films. However this technique has severe limitation to probe structures where there is a poor refractive index contrast between layers. Because of the limited resolution of momentum transfer vector for the Cu K α radiation, it is very difficult to analyse films of very large thicknesses. In the soft x-ray region, near absorption edges, energy-dependent atomic scattering factors have a resonant behaviour [START_REF]Resonant Anomalous X-ray Scattering: Theory and Application[END_REF] and offer an opportunity to enhance the optical index contrast among the thin films. This can be achieved by tuning the energy of x-rays to the absorption edges of specific elements. Soft x-ray reflectivity (SXR) has been used for the characterization of organic and low contrast thin films 2 .

Al/ZrC/Al/W structure is found difficult to get analysed by GIXR because of the large thickness of the overall structure and the poor refractive index contrast at the interfaces owing to intermixing. Earlier studies [START_REF] Singh | AIP Conference Proceedings[END_REF][START_REF] Singh | AIP Conference Proceedings[END_REF] carried out using SXR suggested a large intermixing at the Al/ZrC interfaces and an oxidation on top of the Al layer.

In order to make the Al/ZrC system useful for waveguide applications it is required to protect the top Al layer with a non-reacting capping layer and study the resultant interface quality. Thus it is necessary to analyse the nature of the interfaces formed following the intermixing of adjacent materials in order to improve the interface quality for actual devices. In the present study we have incorporated a protective layer of carbon on top of the Al/ZrC/Al/W structure. The top carbon layer is designed to prevent the oxygen interdiffusion from the top of the stack. We have deposited a 5-layer structure C/Al/ZrC/Al/W on a Si substrate. To understand the nature of interface diffusion we have used time of flight secondary ion mass spectroscopy (ToF-SIMS). To obtain reliable structural parameters, SXR measurements are carried out at 5, 6

and 7 nm incident wavelengths at the Reflectivity beamline of Indus-1 synchrotron radiation facility and best fit is obtained using the same parameters for all three wavelengths.

Experimental Procedure

An ToF-SIMS measurements were performed using Cs + ion gun operating at 1 keV (TOFSIMS-5, Iontof) to sputter the sample. However, the analysis was carried using Bi + ions operating at 30 keV, 4.7 pA. The analysis area was 100 µm × 100 µm inside the sputter crater of 300 µm × 300 µm. structural parameters of the different layers are tabulated in the Table 1. The same parameters

Results and Discussions

were valid for three wavelengths (5, 6 and 7 nm) where reflectivity measurements were performed.

The in-depth profiles of the real part of the optical index in the C/Al/ZrC/Al/W waveguide system as obtained by fitting the SXR data are shown in the Figure 4. It is evident that the 50 nm thick W layer is almost uniform except near the Al-on-W and the W-on-substrate interfaces. Near the W-on-Si interface, the presence of a native oxide layer was assumed.

However, it is found that the obtained delta values at this interface are comparatively higher than that of bulk SiO 2 , indicating the incorporation of W into the native oxide layer of substrate. Near the ZrC-on-Al and Al-on-ZrC interfaces, a gradual increase in the delta value is observed instead of a sharp jump, indicating some intermixing of between the ZrC and Al layers.

The SIMS depth profiles of the Al, C, Zr, ZrC and W positive ions are shown in the

Conclusions

In the present study, SIMS and SXR techniques are used to analyse the interface profile and structural parameters of a C/Al/ZrC/Al/W waveguide system. The analysis of the SXR data provides information about the thicknesses of the different layers of the stack as C (7.4 nm) / Al (9.95 nm) / ZrC (7.5 nm) / Al(18.2 nm) / W(55.7 nm). The roughness of the Al-on-ZrC, ZrC-on-Al and Al-on-W interfaces are found in 1.8 -2.4 nm range. The SIMS data suggest that aluminum diffuses towards the top layer and leaves empty space for diffusion from the layer deposited on top of Al layer. This phenomena leads to an asymmetry of the interfaces.

Figure 1 :

 1 Figure 1: Calculated angular and depth distributions of the electric field inside an Al/ZrC/Al/W waveguide structure excited with a 10.25keV incident x-ray beam. The lateral color bar on the right is proportional to the intensity of the electric field.

  photons in the 4-100 nm wavelength region with high flux and moderate spectral resolution (λ/∆λ~200-450). The beamline employs toroidal mirrors for pre-and post-focussing. Different harmonic suppression edge filters are installed in the beamline to reduce higher harmonic contamination6 . The experimental station is a high vacuum reflectometer consisting of a twocircle goniometer to accomplish θ-2θ scans. A soft x-ray silicon photodiode detector (International Radiation Detector Inc.) is used for measuring the reflected intensity. SXR data have been analysed using the Parratt formalism7 . The effect of surface roughness has been taken into account using the Nevot-Croce model 8 .

Figure 2 :

 2 Figure 2: Schematic diagram of the five-layer sample C/Al/ZrC/Al/W deposited onto a silicon substrate.

Figure 3

 3 Figure3shows the measured and fitted soft x-ray reflectivity patterns of the waveguide structure obtained at the three incident wavelengths. Structural parameters, thickness, roughness and delta (δ) and beta (β), i.e. the real and imaginary parts of the optical index respectively of the

Figure 3 :

 3 Figure 3: Measured (dots) and fitted (solid black line) angle-dependent soft x-ray reflectivity patterns of the C/Al/ZrC/Al/W waveguide system for the incident wavelengths of 5, 6 and 7 nm. Curves are vertically shifted for clarity.

Figure 5 .Figure 4 :

 54 Figure 5. The sputtering time scale is not calibrated to get the direct information about the thickness value. The regions corresponding to the C, Al, ZrC and W layers as well as the substrate are marked by the vertical lines. The carbon ion curve suggests that carbon is diffusing towards the first Al layer. The presence of carbon in the ZrC layer can be due to the presence of the unreacted carbon atoms in the ZrC layer itself. The Al profile suggests that aluminum

Figure 5 :

 5 Figure 5: Time of flight secondary ion mass spectrometry profiles of the C/Al/ZrC/Al/W waveguide system.

Table 1 :

 1 Structural parameters of the different layers in the analysis of the soft x-ray reflectivity data of the C/Al/ZrC/Al/W waveguide system.

	Layer	Thickness (nm)	Roughness σ(nm)
	C	7.4	1.9
	Al	9.95	1.8
	ZrC	7.53	1.8
	Al	18.3	2.1
	W	55.7	2.4
	SiO	4.5	0.9
	2
	Si sub	--	0.4

Acknowledgements

Authors are thankful to Dr. Sanjay Rai and Mr. Rajnish Dhawan for proving samples and to Mr. R. K Gupta for help in soft x-ray reflectivity measurements.