Ces dernières années, des problèmes de fonctionnement dus à la formation de dépôts de soufre élémentaire ont été rapportés dans les réseaux de transport du gaz naturel. La compréhension du phénomène passe par la connaissance de la solubilité du soufre dans ces conditions de pression et de température. Des données sont disponibles seulement dans les conditions de gisement du gaz naturel. Un appareillage expérimental a donc été conçu pour mesurer la solubilité du soufre dans les conditions de transport du gaz naturel. Le protocole de mesure se décompose en trois étapes. La première consiste saturer un gaz en soufre dans une cellule d’équilibre. Une fois l’équilibre solide/gaz établi, le gaz saturé est évacué et bulle dans une solution de piégeage qui capture le soufre dissout par absorption réactive. La dernière étape consiste en la quantification indirecte du soufre présent dans la solution de piégeage par chromatographie en phase gazeuse / Over recent years, many problems of elemental sulphur deposits in natural gas transmission line systems have been notified. These problems occur very often immediately downstream of a pressure reduction facility. In order to prevent the apparition of solid sulphur deposits causing security and maintenance problems it is imperative to determine sulphur solubility in natural gas at pressures and temperatures corresponding to transport conditions. For this work, an original experimental apparatus was designed to measure sulphur solubility in natural gas. The protocol principle is schematically divided into three steps: saturation, trapping and quantification. During the first step solid/gas equilibrium is established between the studied gas and the solid sulphur. Then the saturated gas is evacuated and pass through three separators where sulphur is trapped by reactive absorption. Finally the trapping solution are analyzed by gas chromatography to determine the solubility.

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).