48 research outputs found

    Barjac – Le site gallo-romain des Cayres

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    Date de l'opĂ©ration : 1992 - 1993 (SU) Inventeur(s) : Fages Gilbert (SRA) Des vestiges antiques au hameau des Cayres sont mentionnĂ©s dĂšs 1856 par l’abbĂ© Bosse qui « a vu dans les fondations d’une maison, une couche assez Ă©paisse de dĂ©bris de briques, poteries, etc. ». PrĂšs d’un siĂšcle plus tard, l’adduction d’eau sur le tracĂ© de la route rappelle l’information et en prĂ©cise la localisation « les Cayres, Ă  hauteur de la premiĂšre maison en venant de Barjac ». Mais c’est la construction rĂ©cente ..

    Saint-Georges-de-LĂ©vejac

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    Date de l'opĂ©ration : 1995 (SU) Inventeur(s) : Fages Gilbert (SRA) La grotte II du Valat del Malclapio est nichĂ©e dans la falaise supĂ©rieure formant corniche du versant mĂ©ridional du causse de Sauveterre baignĂ© par le Tarn. En fait, le site couronne un secteur particuliĂšrement pittoresque des gorges au dĂ©bouchĂ© aval des « DĂ©troits » oĂč des Ă©pandages alluviaux ont naguĂšre fixĂ© plusieurs exploitations agricoles : la Croze, aujourd’hui hameau de villĂ©giature. L’opĂ©ration fait suite Ă  une prospec..

    Inventaire et révision archéologique de la LozÚre

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    Date de l'opération : 1992 - 1998 (PR) Inventeur(s) : Fages Gilbert (SRA) Cette opération à finalité patrimoniale, reconduite annuellement, est d'abord alimentée par le suivi des découvertes fortuites du département y compris spéléologiques et les informations de toutes natures communiquées par divers informateurs locaux ou occasionnels. Chaque site validé débouche sur une notice descriptive liée aux documents de localisation. Elle profite aussi des retombées de l'instruction des dossiers de..

    Canourgue (La)

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    Date de l'opĂ©ration : 1995 (SU) Inventeur(s) : Fages Gilbert (SRA) ; Chardonnet Christophe Le site de Pont-Plan est en bordure occidentale du causse de Sauveterre Ă  5,5 km au sud-est du bourg de la Canourgue, non loin du seuil du Malpas, Ă  l’origine du vallon de l’Urugne. Il se situe dans l’accotement nord de la route dĂ©partementale RD 998, entre la Canourgue et Sainte-Enimie, et au dĂ©part du chemin des Crouzets. Il est quasiment en fond de vallĂ©e calcaire occupĂ© par une laniĂšre de terre cult..

    Sainte-Enimie – Le tumulus du Devez Viel

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    Date de l'opĂ©ration : 1991 - 1992 (SU) ; 1993 (SU) Inventeur(s) : Fages Gilbert (SRA) ; Vacquier Jacques Sur le causse de Sauveterre et au nord-est du hameau du mĂȘme nom, le site archĂ©ologique du Devez Viel occupe l’aire sommitale d’une croupe dominante. L’altitude du replat supĂ©rieur est comprise entre 1 010 m et 1 050 m. Cette sorte de petit plateau est structurĂ© par deux modelĂ©s karstiques (sotch) distants d’une centaine de mĂštres. Il livre en surface quelques tessons Ă©rodĂ©s et des piĂšces ..

    Montbrun – Abri sĂ©pulcral du Sot de la Lavogne

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    Date de l'opĂ©ration : 1992 (SU) ; 1993 - 1994 (SU) ; 1996 (SU) ; 1993 - 1996 (SP) Inventeur(s) : Fages Gilbert (SRA) ; Courtaud Patrice ; Le Filattre Virginie La sĂ©pulture collective sous abri du Sot de la Lavogne est situĂ©e tout prĂšs du rebord septentrional du causse MĂ©jean surplombant les gorges du Tarn entre les bourgs de Montbrun et de Castelbouc. Au sein d’une zone dolomitisĂ©e, elle est installĂ©e au creux d’un renfoncement naturel de la paroi rocheuse qui borde au nord une modeste doline..

    Étude carpologique des rĂ©serves vĂ©gĂ©tales de la grotte de Baume Layrou (TrĂšves, Gard)

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    De nouvelles analyses carpologiques sur la Baume Layrou viennent complĂ©ter et prĂ©ciser les informations dĂ©jĂ  recueillies par J. Erroux sur les stocks entreposĂ©s dans la grotte. La trĂšs forte densitĂ© en carporestes et la domination de quelques plantes cultivĂ©es permettent de conclure que ces rĂ©serves Ă©taient constituĂ©es d’épeautre (Triticum spelta), de millet commun (Panicum miliaceum), d’orge (Hordeum vulgare), d’amidonnier (T. dicoccum) ainsi que, vraisemblablement, de blĂ© nu (T. aestivum/turgidum) et de fĂ©verole (Vicia faba). Les cĂ©rĂ©ales Ă©taient largement traitĂ©es et nettoyĂ©es. L’écologie des mauvaises herbes indique que le millet Ă©tait semĂ© au printemps et rĂ©coltĂ© par coupe haute alors que la totalitĂ© des autres cĂ©rĂ©ales Ă©tait probablement semĂ©e Ă  l’automne et moissonnĂ©e Ă  une hauteur relativement basse. La germination accidentelle d’une petite partie des grains et l’état dĂ©cortiquĂ© des blĂ©s vĂȘtus nous conduisent Ă  conclure que la grotte ne constituait pas un lieu de stockage Ă  long terme mais plus probablement un lieu de stockage liĂ© Ă  un habitat temporaire, peut ĂȘtre un refuge.An archaeobotanical study of the remains from the Baume Layrou cave site (TrĂšves, Gard). Elements of agricultural production and the function of a Bronze Age site.After the previous archaeobotanical work carried out by J. Erroux, further carpological investigation of Baume Layrou is bringing new information about crop storage in the cave. Very high carpological density and dominance of a few domesticated taxa show that spelt (Triticum spelta), common millet (Panicum miliaceum), hulled barley (Hordeum vulgare), emmer (T. dicoccum), as well as, possibly, naked wheat (T. aestivum/turgidum) and horse bean (Vicia faba) were stored in the cave. Cereals were largely cleaned and processed. Weed ecology shows that common millet was spring sown and harvested by high level reaping. On the other hand, all the other cereal species seem to have been sown during autumn and harvested by mean of a fairly low reaping. Accidental sprouting of some kernels as well as storage of dehusked glume wheats do not agree with the hypothesis of long term usual storage in the cave. It was more likely used as an occasional habitat and storage place, maybe as a refuge

    A model checking approach to the parameter estimation of biochemical pathways

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    Model checking has historically been an important tool to verify models of a wide variety of systems. Typically a model has to exhibit certain properties to be classed ‘acceptable’. In this work we use model checking in a new setting; parameter estimation. We characterise the desired behaviour of a model in a temporal logic property and alter the model to make it conform to the property (determined through model checking). We have implemented a computational system called MC2(GA) which pairs a model checker with a genetic algorithm. To drive parameter estimation, the fitness of set of parameters in a model is the inverse of the distance between its actual behaviour and the desired behaviour. The model checker used is the simulation-based Monte Carlo Model Checker for Probabilistic Linear-time Temporal Logic with numerical constraints, MC2(PLTLc). Numerical constraints as well as the overall probability of the behaviour expressed in temporal logic are used to minimise the behavioural distance. We define the theory underlying our parameter estimation approach in both the stochastic and continuous worlds. We apply our approach to biochemical systems and present an illustrative example where we estimate the kinetic rate constants in a continuous model of a signalling pathway

    A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

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    Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems

    Continuous crystallisation

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    Although crystallisation in pharmaceutical manufacturing is traditionally carried out as a batch operation, with the drive towards implementing continuous manufacturing of pharmaceuticals there is increased interest in developing and applying approaches for continuous crystallisation [1, 2]. Indeed, the potential to directly connect multiple process stages as part of an integrated end-to-end process chain including a continuous crystallisation step has been demonstrated for the manufacture of aliskiren hemifumarate tablets [3] and in a compact reconfigurable platform for a range of liquid dosage APIs [4]. Crystallisation is a key operation for the purification and isolation of active pharmaceutical ingredients (APIs) from solution mixtures to produce pure drug substance in a stable, solid form suitable for subsequent formulation and processing. Crystallisation is therefore a critical stage in controlling the physical properties of the solid material [5, 6]. For pharmaceuticals, achieving high levels of chemical purity of crystallised or precipitated particles is an essential requirement. However, a given API can also show a range of variability in crystalline form (polymorph, solvate, salt, co-crystal), crystal size, size distribution and shape that can have significant effects on processing performance and product stability [7]. Consequently, robust continuous crystallisation processes are required that can achieve the target particle attributes consistently and avoid uncontrolled variation in quality and performance. However, despite the widespread application of crystallisation in fine chemical and pharmaceutical production, it still remains relatively poorly understood. Hence the development of consistent and robust continuous crystallisation processes requires systematic and rigorous approaches to identify and control the complex physical transformations that take place within a multicomponent, multiphase process environment
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