Jesús Asín

Quantification of angiogenic sprouting under different growth factors in a microfluidic platform

Angiogenesis, as example of collective migration of endothelial cells (ECs), is the main dynamic process that culminates in sprout formation from existing vessels. After tissue injury, the vascularity is interrupted, triggering the regeneration process and the release of different growth factors (GFs). The main aim of this work is to quantify the effect of specific GFs during the initial stage of sprout formation, namely: VEGF, PDGF-BB, TGFβ and BMP-2, all of them involved in regenerative processes. For this purpose, we designed a novel algorithm implemented in Matlab to quantify the advance of the EC monolayer over time and the sprout structure in 3D. Our results show that VEGF is the main regulatory GF on angiogenesis processes, producing longer sprouts with higher frequency. However, the chemoattractant effect of VEGF depends on its concentration and its spatiotemporal location, having a critical impact on the sprout time evolution. PDGF-BB (namely as PDGF) has a global negative effect on both the number and length of sprouts. TGFβ enhances sprout length at earlier times, although its effect gradually disappears over time. Finally, BMP-2 produces, overall, less number and shorter sprouts, but was the only GF with a positive evolution at longer times, producing fewer but longer sprouts.

Modeling and projecting the occurrence of bivariate extreme heat events using a non-homogeneous common Poisson shock process

A joint model is proposed for analyzing and predicting the occurrence of extreme heat events in two temperature series, these being daily maximum and minimum temperatures. Extreme heat events are defined using a threshold approach and the suggested model, a non-homogeneous common Poisson shock process, accounts for the mutual dependence between the extreme events in the two series. This model is used to study the time evolution of the occurrence of extreme events and its relationship with temperature predictors. A wide range of tools for validating the model is provided, including influence analysis. The main application of this model is to obtain medium-term local projections of the occurrence of extreme heat events in a climate change scenario. Future temperature trajectories from general circulation models, conveniently downscaled, are used as predictors of the model. These trajectories show a generalized increase in temperatures, which may lead to extrapolation errors when the model is used to obtain projections. Various solutions for dealing with this problem are suggested. The results of the fitted model for the temperature series in Barcelona in 1951–2005 and future projections of extreme heat events for the period 2031–2060 are discussed, using three global circulation model trajectories under the SRES A1B scenario.

A bootstrap test of independence between three temporal nonhomogeneous Poisson processes and its application to heat wave modeling

The main contribution of this work is a bootstrap test to check the independence between temporal nonhomogeneous Poisson processes. The test statistic is based on the close point relation, which adapts the crossed nearest neighbour distance ideas of spatial point processes to the case of nonhomogeneous time point processes. Since it is complicated to obtain the probability distribution of the test statistic under the null hypothesis and the parameters of the processes are usually unknown, the

Dynamic Regression Model for Hourly River Level Forecasting Under Risk Situations: an Application to the Ebro River

p

Quantification of sprouting angiogenesis under the effect of different growth factors involved in the tumor microenvironmen

p value is obtained using a parametric bootstrap approach. A simulation study shows that the size of the test is close to the nominal one. The power is analyzed considering three approaches for generating dependent nonhomogeneous processes and different levels of dependence, and satisfactory results are obtained in all cases. Although the test was initially intended for Poisson processes, it can be applied to any type of point process in one dimension which can be simulated. This test is a valuable tool in the validation analysis of common Poisson shock models. For the bivariate case, the process can be decomposed into three independent Poisson processes, and the assumption of independence between them has to be checked. As an application, the joint modeling of the occurrence process of extreme heat events in daily maximum and minimum temperatures is described.

Forecasting local daily precipitation patterns in a climate change scenario

Osteoblast migration is a crucial process in bone regeneration, which is strongly regulated by interstitial fluid flow. However, the exact role that such flow exerts on osteoblast migration is still unclear. To deepen the understanding of this phenomenon, we cultured human osteoblasts on 3D microfluidic devices under different fluid flow regimes. Our results show that a slow fluid flow rate by itself is not able to alter the 3D migratory patterns of osteoblasts in collagen-based gels but that at higher fluid flow rates (increased flow velocity) may indirectly influence cell movement by altering the collagen microstructure. In fact, we observed that high fluid flow rates (1 µl/min) are able to alter the collagen matrix architecture and to indirectly modulate the migration pattern. However, when these collagen scaffolds were crosslinked with a chemical crosslinker, specifically, transglutaminase II, we did not find significant alterations in the scaffold architecture or in osteoblast movement. Therefore, our data suggest that high interstitial fluid flow rates can regulate osteoblast migration by means of modifying the orientation of collagen fibers. Together, these results highlight the crucial role of the matrix architecture in 3D osteoblast migration. In addition, we show that interstitial fluid flow in conjunction with the matrix architecture regulates the osteoblast morphology in 3D.

Trend analysis of water quality series based on regression models with correlated errors

This work proposes a new statistical modelling approach to forecast the hourly river level at a gauging station, under potential flood risk situations and over a medium-term prediction horizon (around three days). For that aim we introduce a new model, the switching regression model with ARMA errors, which takes into account the serial correlation structure of the hourly level series, and the changing time delay between them. A whole modelling approach is developed, including a two-step estimation, which improves the medium-term prediction performance of the model, and uncertainty measures of the predictions. The proposed model not only provides predictions for longer periods than other statistical models, but also helps to understand the physics of the river, by characterizing the relationship between the river level in a gauging station and its influential factors. This approach is applied to forecast the Ebro River level at Zaragoza (Spain), using as input the series at Tudela. The approach has shown to be useful and the resulting model provides satisfactory hourly predictions, which can be fast and easily updated, together with their confidence intervals. The fitted model outperforms the predictions from other statistical and numerical models, specially in long prediction horizons.

The minimization of the screen bias from ancient Western Mediterranean air temperature records: An exploratory statistical analysis

Unraveling the underlying mechanisms involved in single and collective cell migration, organization, tissue formation and regeneration are some of the currently spearheading research topics worldwide. To overcome the intrinsic difficulties associated to realistic environments in 3D, a multidisciplinary framework combining computer simulations and experiments is proposed. The success of this approach relies on result validation (quantitative comparison: computational vs. experimental), so we have polished different techniques and computational tools to get as accurate measurements as possible. For that, we take advantage of our own designed microfluidic devices and perform thorough image and statistical analyses.