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After recalling the theoretical genealogy of semiotics, between linguistics, anthropology and phenomenology, we would like to highlight the properties of post-Greimassian semiotics by showing that "signification in act", its object, simultaneously involves the capture of formants, their structuring, their enunciation, their putting into perspective, their implications in the social field and their anthropological stakes. We rely on two examples, one textual, the other lexical. The first is a transversal semiotic reading of a text by Victor Hugo, "L'archipel de la Manche", the prologue to his novel Les Travailleurs de la mer (1866, ed. 1883): we show how the great multi-disciplinary fabric of the natural, human and social sciences takes shape in this text through a poetics and a politics of enunciation, the primary and ultimate discipline. The second example deals with the collective act through the use of the pronoun "we", between inclusion and exclusion. We then draw on Paolo Fabbri's (forthcoming) final text, "Collective identities", to question the many ways in which the language form passes between the language form and the politics of the otherness it induces. This lecture is given in homage to this great Italian semiotician, who passed away on June 2, 2020. ; Après avoir rappelé la généalogie théorique de la sémiotique, entre linguistique, anthropologie et phénoménologie, nous souhaitons mettre en évidence les propriétés de la sémiotique post-greimassienne en montrant que la «signification en acte», son objet, implique simultanément la saisie des formants, leur structuration, leur énonciation, leur mise en perspective, leurs implications dans le champ social et leurs enjeux anthropologiques. Nous nous appuyons sur deux exemples, l'un textuel, l'autre lexical. Le premier aura pour objet une lecture sémiotique transversale d'un texte de Victor Hugo, «L'archipel de la Manche», prologue de son romanLes Travailleurs de la mer(1866, éd. de 1883): nous montrons comment la grande trame pluri-disciplinaire des ...

Summary The post-post-memory passes through generations: what remains of the distant trauma? How are memory and long-term forgotten articulated? Based on the massacres of Saint-Barthélemy in 1572, this article calls into question the discursive variations in memory, between remembrance and commemoration, through the examination of three discursive strategies: a historian (Benedict Anderson), a religious (François Clavairoly) and a political character (Anne Hidalgo). Basing the analysis on the theoretical assumptions of semi-otic, we propose a more general reflection on forgetting, which, between temporary wear and political prescriptions, expands its empire. The memory is then presented as resistance and confrontation. The semi-otic approach makes it possible to outline a modelling of this modulation of subjects involved in the post-memory. ; Resumen La pos-posmemoria atraviesa las generaciones: ¿qué es lo que permanece de aquellos lejanos traumas? ¿Cómo se articulan la memoria y el olvido a largo plazo? Basándose en las masacres de San Bartolomé, en 1572, este artículo cuestiona las variaciones discursivas de la memoria, entre la rememoración y la conmemoración, a través del examen de tres estrategias discursivas: la de un historiador (Benedict Anderson), la de un religioso (François Clavairoly) y la de un personaje político (Anne Hidalgo). Fundamentando el análisis en los supuestos teóricos de la semiótica, se propone una reflexión más general sobre el olvido, fenómeno que, entre el desgaste temporal y la prescripción política, expande su imperio. La memoria se presenta entonces como resistencia y confrontación. El enfoque semiótico permite esbozar una modelización de esta modulación de sujetos implicados en la pos-posmemoria.

La spécificité d'une approche sémiotique du politique se situe dans le croisement de discours dont la politique est à la fois l'objet et l'enjeu : au premier rang, comme ils ont en partage la communication, sa relation avec le discours médiatique. Les deux univers entretiennent, à différents niveaux, des rapports complexes de dépendance et de domination qui peuvent être appréhendés sur le mode tensif. A travers quelques motifs représentatifs de cette interaction en crise (narrativité et manipulation, dynamique passionnelle, jeux de présence et d'absence, registres énonciatifs, figurativité et spectacle), on interroge ici plus largement les conditions d'une sémiotique du politique. ; La spécificité d'une approche sémiotique du politique se situe dans le croisement de discours dont la politique est à la fois l'objet et l'enjeu : au premier rang, comme ils ont en partage la communication, sa relation avec le discours médiatique. Les deux univers entretiennent, à différents niveaux, des rapports complexes de dépendance et de domination qui peuvent être appréhendés sur le mode tensif. A travers quelques motifs représentatifs de cette interaction en crise (narrativité et manipulation, dynamique passionnelle, jeux de présence et d'absence, registres énonciatifs, figurativité et spectacle), on interroge ici plus largement les conditions d'une sémiotique du politique.

In a context of deep changing on the complementary health insurance market and the sensitivity of its enterprise data, the executive management of Sphéria Val de France is concerned about securing access to its information system. While it is well protected from external attacks, access to the internal network is not. Today, an ill-intentioned person can try, within the company, to access the local network from their own personal computer. The purpose of this paper is to study and to implement a solution to control physical access to the corporate network by ensuring the identity of equipments to block any intrusion attempts. ; Dans un contexte de grande mutation que connaît le secteur des mutuelles et le caractère sensible des données de l'entreprise, la Direction du groupe Sphéria Val de France s'inquiète de sécuriser l'accès à son système d'information. Alors que celui-ci est bien protégé des attaques extérieures, l'accès au réseau en interne ne l'est pas. Aujourd'hui, une personne mal intentionnée peut tenter de l'intérieur de l'entreprise d'accéder au réseau informatique depuis son ordinateur personnel. L'objet de ce mémoire est donc d'étudier et de mettre en œuvre une solution permettant de contrôler l'accès physique au réseau de l'entreprise en garantissant l'identité des équipements afin de bloquer toute tentative d'intrusion.

Les auteurs définissent le système universitaire et s'efforcent d'identifier au sein de celui-ci les domaines d'intervention de l'État. Ils resituent les rapports de l'État et du monde universitaire au cours des trente dernières années en retraçant la place occupée par les politiques scientifiques et technologiques québécoises et canadiennes. Ils comparent les tendances des politiques publiques au Canada et aux États-Unis et les changements survenus dans les systèmes universitaires européens. Ils analysent, ensuite, la démocratisation et le financement des universités et évaluent le rôle des d

WOS: 000471758500010 ; PubMed ID: 31209238 ; The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZenecaAstraZeneca; European Union Horizon 2020 research [668858 PrECISE]; Joint Research Center for Computational Biomedicine (Bayer AG); National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome TrustWellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZeneca ; European Union Horizon 2020 research [668858 PrECISE] ; Joint Research Center for Computational Biomedicine (Bayer AG) ; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences ; Wellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).