Adriano Francisco Siqueira

arXiv2019

Stochastic modeling of hyposmotic lysis and characterization of different osmotic stability subgroups of human erythrocytes

This study proposes a novel stochastic model for the study of hyposmotic hemolysis. This model is capable of reproducing both the kinetics in the transient phase and the lysis equilibrium in the stationary phase, as well as the variability of the experimental measurements. The stationary distribution of this model can be approximated to a normal distribution, with mean and variance related to the salt concentration used in the erythrocyte osmotic fragility assay. The proposed model can generalize the classical Boltzmann sigmoidal model often used in adjusting the stationary experimental data distribution. A typical osmotic fragility curve is constructed from the absorbance of free hemoglobin as a function of the decrease in NaCl (X) concentration and allows the determination of H50, an osmotic fragility variable that represents the saline concentration capable of promoting 50% lysis, and dX, an osmotic stability variable that represents 1/4 of the variation in salt concentration required to promote 100% lysis. Based on the stationary distribution of the proposed model it is possible to stratify a population into different groups of individuals with similar levels of cell stability. These groups are very suitable to study the factors associated with cell stability, such as gender, age and lipids, among others. The method presented here was applied to a sample of 71 individuals and several results were obtained. In a group of 25 female subjects, with H50 values between 0.42 and 0.47 g/dL NaCl, for example, the use of a quadratic model to study the dependence of the stability index dX/H50 with blood LDL-cholesterol levels, showed that the erythrocyte osmotic stability increases with increasing LDL-C to a maximum value close to 90 mg/dL and then decreases.

q-bio.QM↗

arXiv2016

Effect of stochastic transition in the fundamental diagram of traffic flow

In this work, we propose an alternative stochastic model for the fundamental diagram of traffic flow with minimal number of parameters. Our approach is based on a mesoscopic viewpoint of the traffic system in terms of the dynamics of vehicle speed transitions. A key feature of the present approach lies in its stochastic nature which makes it possible to study not only the flow-concentration relation, namely, the fundamental diagram, but also its uncertainty, namely, the variance of the fundamental diagram \textemdash an important characteristic in the observed traffic flow data. It is shown that in the simplified versions of the model consisting of only a few speed states, analytic solutions for both quantities can be obtained, which facilitate the discussion of the corresponding physical content. We also show that the effect of vehicle size can be included into the model by introducing the maximal congestion density $k_{max}$. By making use of this parameter, the free flow region and congested flow region are naturally divided, and the transition is characterized by the capacity drop at the maximum of the flow-concentration relation. The model parameters are then adjusted to the observed traffic flow on the I-80 Freeway Dataset in the San Francisco area from the NGSIM program, where both the fundamental diagram and its variance are reasonably reproduced. Despite its simplicity, we argue that the current model provides an alternative description for the fundamental diagram and its uncertainty in the study of traffic flow.

physics.soc-ph↗