Fluorescent dextrans are commonly used as macropinocytic probes to study the properties of endocytic cargoes; however, the effect of the size of dextrans on endocytic mechanisms has not been carefully analyzed. By using chemical and siRNA inhibition of individual endocytic pathways, we evaluated the internalization of two commonly used dextrans, Dex10 (dextran 10 kDa) and Dex70 (dextran 70 kDa), in mammalian HeLa cells and Caenorhabditis elegans coelomocytes. We revealed that Dex70 enters these two cell types predominantly via clathrin- and dynamin-independent and amiloride-sensitive macropinocytosis process; Dex10, on the other hand, enters the two cell types through clathrin-/dynamin-dependent micropinocytosis in addition to macropinocytosis. In addition, although different-sized dextrans follow different endocytic processes, they share common post-endocytic events. Herein, though straightforward, our studies support that the size of nanomaterials could play a paramount role in their inclusion into endocytic vesicles and suggest that care should be taken while selecting endocytic pathway markers. Based on our results, we propose that Dex70 is a better probe for macropinocytosis, whereas Dex10 and smaller molecules are better for probing general fluid-phase endocytosis, which includes macropinocytic and micropinocytic processes.

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Invasive fractional flow reserve (FFR) is the gold standard to assess the functional coronary stenosis. The non-invasive assessment of diameter stenosis (DS) using coronary computed tomography angiography (CTA) has high false positive rate in contrast to FFR. Combining CTA with computational fluid dynamics (CFD), recent studies have shown promising predictions of FFRCT for superior assessment of lesion severity over CTA alone. The CFD models tend to be computationally expensive, however, and require several hours for completing analysis. Here, we introduce simplified models to predict noninvasive FFR at substantially less computational time. In this retrospective pilot study, 21 patients received coronary CTA. Subsequently a total of 32 vessels underwent invasive FFR measurement. For each vessel, FFR based on steady-state and analytical models (FFRSS and FFRAM, respectively) were calculated non-invasively based on CTA and compared with FFR. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 90.6% (87.5%), 80.0% (80.0%), 95.5% (90.9%), 88.9% (80.0%) and 91.3% (90.9%) respectively for FFRSS (and FFRAM) on a per-vessel basis, and were 75.0%, 50.0%, 86.4%, 62.5% and 79.2% respectively for DS. The area under the receiver operating characteristic curve (AUC) was 0.963, 0.954 and 0.741 for FFRSS, FFRAM and DS respectively, on a per-patient level. The results suggest that the CTA-derived FFRSS performed well in contrast to invasive FFR and they had better diagnostic performance than DS from CTA in the identification of functionally significant lesions. In contrast to FFRCT, FFRSS requires much less computational time.

To reflect the uncertainties of a hydrological model in simulating and forecasting observed discharges according to rainfall inputs, the estimated result for each time step should not be just a point estimate (a single numerical value), but should be expressed as a prediction interval, i.e. a band defined by the prediction bounds of a particular confidence level α. How best to assess the quality of the prediction bounds thus becomes very important for understanding the modelling uncertainty in a comprehensive and objective way. This paper focuses on seven indices for characterizing the prediction bounds from different perspectives. For the three case-study catchments presented, these indices are calculated for the prediction bounds generated by the generalized likelihood uncertainty estimation (GLUE) method for various threshold values. In addition, the relationships among these indices are investigated, particularly that of the containing ratio (CR) to the other indices. In this context, three main findings are obtained for the prediction bounds estimated by GLUE. Firstly, both the average band-width and the average relative band-width are seen to have very strong linear correlations with the CR index. Secondly, a high CR value, a narrow band-width, and a high degree of symmetry with respect to the observed hydrograph, all of which are clearly desirable properties of the prediction bounds estimated by the uncertainty assessment methods, cannot all be achieved simultaneously. Thirdly, for the prediction bounds considered, the higher CR values and the higher degrees of symmetry with respect to the observed hydrograph are found to be associated with both the larger band-widths and the larger deviation amplitudes. It is recommended that a set of different indices, such as those considered in this study, be employed for assessing and comparing the prediction bounds in a more comprehensive and objective way. Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en oeuvre pour une estimation d'incertitude par vraisemblance généraliséeRésumé Afin de refléter les incertitudes d'un modèle hydrologique lors de la simulation et de la prévision de débits à partir de données de pluie, le résultat estimé à chaque pas de temps ne devrait pas être juste un point (une unique valeur numérique), mais devrait être exprimé sous la forme d'un intervalle de prévision, c'est-à-dire une bande définie par des bornes de prévision associées à un niveau de confiance particulier α. Estimer au mieux la qualité des bornes de prévision devient ainsi très important pour comprendre l'incertitude de modél-isation dans un sens complet et objectif. Cet article s'intéresse à sept indices pour caractériser les bornes de prévision, selon différentes perspectives. Pour les trois bassins versants présentés, ces indices sont calculés pour les bornes de prévision générées par la méthode GLUE, pour plusieurs valeurs de seuillage. De plus, les relations entre ces indices sont étudiées, en particulier entre le rapport de recouvrement (R...

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