Chaussures minimalistes: quelle influence sur le taux de blessures et les douleurs ? : travail de bachelor

INTRODUCTION : Depuis quelques dizaines d’années, la course à pied est un sport à la popularité grandissante. Toutefois, sa pratique n'est pas sans risque et les blessures inhérentes à ce sport interpellent. Les avantages de la chaussure standard sont remis en question avec l'essor de la chaussure minimaliste. PROBLÉMATIQUE: Évaluer le taux de blessures et l’intensité de la douleur ressentie chez les coureurs récréatifs, lors d’une transition de chaussures standard vers des chaussures minimalistes. MÉTHODE : Nous avons consulté les bases de données Medline via PubMed, Cinahl, PEDro, Embase, Cochrane Library, LiSSa, BDSP et Kinedoc. La stratégie de recherche utilisée comprenait des combinaisons de mots-clefs et de termes bruts concentrées sur trois thèmes: la course à pied, les chaussures minimalistes et les blessures. Trois études ont été retenues: deux études randomisées controlées et une étude prospective. Leurs qualités ont été évaluées avec l’échelle PEDro et la checklist CONSORT. RÉSULTATS : Au sujet du risque de blessures, la différence entre les groupes chaussures conventionnelles et chaussures minimalistes est non significative, même si elle tend vers un nombre plus important de blessés lors de la transition vers les chaussures minimalistes. En ce qui concerne les douleurs globales, celles-ci ne diffèrent pas entre les groupes. Par contre, nous observons que le coureur effectuant une transition vers les chaussures minimalistes peut subir des douleurs plus importantes, au mollet et au tibia principalement. CONCLUSION : Le respect d’une transition lente semble diminuer le risque de blessures. Cependant, des études complémentaires seraient nécessaires afin d’établir des directives sur la protection des coureurs pendant cette période

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Scene Linking Annotation and Automatic Scene Characterization in TV Series

International audienceIn the context of a large collection of multimedia documents, creating links between documents or scenes can help to organize the collection. For TV series, this organization can be achieved by means of narrative structure extraction through scene linking. Narrative characteristics such as speaking characters, entity mentions and theme can be used to characterize scenes. The linking of scenes can be between scenes inside an episode, between scenes in di↵erent episodes and/or in di↵erent seasons, since stories in TV series progress at di↵erent level of granularity. In this work, we have annotated the links between the scenes of the first two seasons of the TV series Game of Thrones, using predefined stories and sub stories. We have also automatically extracted the narrative characteristics of each scene. The dataset is composed by 444 scenes, involving 154 speaking character organized in 46 stories divided into 151 sub stories and 5 sub sub-stories

Détection de scènes remarquables dans un contexte de séries TV

National audienceTo access a large amount of multimedia data, it is often useful to extract a summaryor the most salient element from the collection. In TV series, one way to extract the summaryof an episode is to detect the most reportable scenes, i.e. those which make a radical change tothe narrative of an episode, before combining them to produce a summary of the episode, theseason, or the entire series. The reportable aspect of a scene or, more broadly of a multimediadocument, is carried by its different modalities - text, speech and image - in a joint way or not.In addition, a scene can only be defined as reportable in comparison to its surrounding scenes.We present in this article the first results on the combination of the different modalities and theaccounting for the context to extract the most reportable scenes of the first two seasons of theGame of Thrones TV series. We show that the use of context and multimodality can improve thedetection of most reportable scene.Pour faciliter l'accès à une large quantité de données multimédia, il est souvent utile d'en extraire un résumé ou l'élément le plus saillant. Dans le cadre des séries télévisées, une manière d'extraire le résumé d'un épisode consiste à détecter les scènes les plus remarquables, c'est-à-dire celles qui apportent un changement radical au récit d'un épisode, avant de les combiner pour produire un résumé de l'épisode, de la saison ou de la série entière. L'aspect remarquable d'une scène ou, plus largement d'un document multimédia, est porté par ses différentes modalités-texte, parole et image-de façon conjointe ou non. Par ailleurs, une scène ne peut se définir comme remarquable qu'en comparaison des scènes qui l'entourent. Nous présentons dans cet article les premiers résultats sur la combinaison des différentes modalités et de la prise en compte du contexte pour extraire les scènes remarquables des deux premières saisons de la série Game of Thrones. Nous montrons que l'utilisation du contexte et de la prise en compte de la multimodalité permettent d'améliorer la détection de scènes remarquables

Scene Linking Annotation and Automatic Scene Characterization in TV Series

International audienceIn the context of a large collection of multimedia documents, creating links between documents or scenes can help to organize the collection. For TV series, this organization can be achieved by means of narrative structure extraction through scene linking. Narrative characteristics such as speaking characters, entity mentions and theme can be used to characterize scenes. The linking of scenes can be between scenes inside an episode, between scenes in di↵erent episodes and/or in di↵erent seasons, since stories in TV series progress at di↵erent level of granularity. In this work, we have annotated the links between the scenes of the first two seasons of the TV series Game of Thrones, using predefined stories and sub stories. We have also automatically extracted the narrative characteristics of each scene. The dataset is composed by 444 scenes, involving 154 speaking character organized in 46 stories divided into 151 sub stories and 5 sub sub-stories

Video Scene Segmentation of TV Series Using Multimodal Neural Features

Scene segmentation of a video, a book or TV series allows to organize them into Logical Story Units and is an essential step for representing, extracting and understanding their narrative structures. We propose an automatic scene segmentation method for TV series based on the grouping of adjacent shots and relying on a combination of multimodal neural features: visual features and textual features, further augmented with the temporal information which may improve the clustering of adjacent shots. Reported experiments compare early and late fusion of the features, video frames subsampling and various shot clustering algorithms. The proposed method achieved good recall, precision and F-measure when tested on several seasons of two popular TV series

Survey on Narrative Structure: from Linguistic Theories to Automatic Extraction Approaches

International audienceNarration is an essential element in the transmission of written and oral stories and corresponds to both who is telling the story and how the story is told. Philosophers and structuralists have analyzed and defined different narrative structures. Recently, researchers in Machine Learning (ML) or Natural Language Processing (NLP) have been particularly interested on the extraction and understanding of narrative structure in different collections. On this work, we present a survey on theories, research and techniques around narrative structure: from linguistic theories to automatic approaches used for the extraction and analysis of narrative structure in different multimedia collections and annotation tools

Multimodal person discovery in broadcast tv at mediaeval 2015

ABSTRACT We describe the "Multimodal Person Discovery in Broadcast TV" task of MediaEval 2016 benchmarking initiative. Participants are asked to return the names of people who can be both seen as well as heard in every shot of a collection of videos. The list of people is not known a priori and their names has to be discovered in an unsupervised way from media content using text overlay or speech transcripts for the primary runs. The task is evaluated using information retrieval metrics, based on a posteriori collaborative annotation of the test corpus

Commercial Lysogeny Broth culture media and oxidative stress: A cautious tale

International audienc