Interdependence of cholesterol distribution and conformational order in lipid bilayers.

We show, via molecular simulations, that not only does cholesterol induce a lipid order, but the lipid order also enhances cholesterol localization within the lipid leaflets. Therefore, there is a strong interdependence between these two phenomena. In the ordered phase, cholesterol molecules are predominantly present in the bilayer leaflets and orient themselves parallel to the bilayer normal. In the disordered phase, cholesterol molecules are mainly present near the center of the bilayer at the midplane region and are oriented orthogonal to the bilayer normal. At the melting temperature of the lipid bilayers, cholesterol concentration in the leaflets and the bilayer midplane is equal. This result suggests that the localization of cholesterol in the lipid bilayers is mainly dictated by the degree of ordering of the lipid bilayer. We validate our findings on 18 different lipid bilayer systems, obtained from three different phospholipid bilayers with varying concentrations of cholesterol. To cover a large temperature range in simulations, we employ the Dry Martini force field. We demonstrate that the Dry and the Wet Martini (with polarizable water) force fields produce comparable results.

Mesoscale Aggregation of Sulfur-Rich Asphaltenes: In Situ Microscopy and Coarse-Grained Molecular Simulation.

Asphaltene aggregation is critical to many natural and industrial processes, from groundwater contamination and remediation to petroleum utilization. Despite extensive research in the past few decades, the fundamental process of sulfur-rich asphaltene aggregation still remains not fully understood. In this work, we have investigated the particle-by-particle growth of aggregates formed with sulfur-rich asphaltene by a combined approach of in situ microscopy and molecular simulation. The experimental results show that aggregates assembled from sulfur-rich asphaltene have morphologies with time-dependent structural self-similarity, and their growth rates are aligned with a crossover behavior between classic reaction-limited aggregation and diffusion-limited aggregation. Although the particle size distribution predicted using the Smoluchowski equation deviates from the observations at the initial stage, it provides a reasonable prediction of aggregate size distribution at the later stage, even if the observed cluster coalescence has an important effect on the corresponding cluster size distribution. The simulation results show that aliphatic sulfur exerts nonmonotonic effects on asphaltene nanoaggregate formation depending on the asphaltene molecular structure. Specifically, aliphatic sulfur has a profound effect on the structure of rod-like nanoaggregates, especially when asphaltene molecules have small aromatic cores. Interactions between aliphatic sulfur and the side chain of neighboring molecules account for the repulsive forces that largely explain the polydispersity in the nanoaggregates and corresponding colloidal aggregates. These results can improve our current understanding of the complex process of sulfur-rich asphaltene aggregation and sheds light on designing efficient crude oil utilization and remediation technologies.

Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models.

The overarching goal of this paper is to accurately forecast ATM cash demand for periods both before and during the COVID-19 pandemic. To achieve this, first, ATMs are categorized based on accessibility and surrounding environmental factors that significantly affect the cash withdrawal pattern. Then, several statistical and machine learning models under different algorithms and strategies are employed. In aiming to provide the feature matrix for machine learning models, some new influential variables are added to the literature. Finally, a modified fitness measure is proposed for the first time to correctly choose the most promising model by considering both the prediction errors and accuracy of direction's change simultaneously. The results obtained by a comprehensive analysis-a statistical analysis together with grid search and k-fold cross-validation techniques-reveal that (i) category-wise prediction enhances forecasting quality; (ii) before COVID-19 and in times when there are only minor disturbances in withdrawal patterns, forecasting quality is higher, and in general, the machine learning models can more appropriately forecast ATM's cash demand; (iii) despite studies in the literature, sophisticated models will not always outperform simpler models. It is found that during COVID-19 and in times when there is a sudden shock in demand and massive volatility in withdrawal patterns, the statistical models of the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) can mainly provide better forecasting likely due to high performance of such models for short-term prediction, while minimizing overfitting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-01000-0.

Quantitative relationship between cholesterol distribution and ordering of lipids in asymmetric lipid bilayers.

The plasma membrane of eukaryotic cells is known to be compositionally asymmetric. Certain phospholipids, such as sphingomyelin and phosphatidylcholine species, are predominantly localized in the outer leaflet, while phosphatidylethanolamine and phosphatidylserine species primarily reside in the inner leaflet. While phospholipid asymmetry between the membrane leaflets is well established, there is no consensus about cholesterol distribution between the two leaflets. We have performed a systematic study, via molecular simulations, of how the spatial distribution of cholesterol molecules in different "asymmetric" lipid bilayers are affected by the lipids' backbone, head-type, unsaturation, and chain-length by considering an asymmetric bilayer mimicking the plasma membrane lipids of red blood cells, as well as seventeen other asymmetric bilayers comprising of different lipid types. Our results reveal that the distribution of cholesterol in the leaflets is solely a function of the extent of ordering of the lipids within the leaflets. The ratio of the amount of cholesterol matches the ratio of lipid order in the two leaflets, thus providing a quantitative relationship between the two. These results are understood by the observation that asymmetric bilayers with equimolar amount of lipids in the two leaflets develop tensile and compressive stresses due to differences in the extent of lipid order. These stresses are alleviated by the transfer of cholesterol from the leaflet in compressive stress to the one in tensile stress. These findings are important in understanding the biology of the cell membrane, especially with regard to the composition of the membrane leaflets.

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures.

The plasma membrane of eukaryotic cells is commonly believed to contain ordered lipid domains. The interest in understanding the origin of such domains has led to extensive studies on the phase behavior of mixed lipid systems. Three-component phase diagrams, composed of a high melting temperature (Tm) lipid, cholesterol, and a low Tm lipid have been valuable in studying lipid phase behavior. However, developing phase diagrams over the entire composition space and with precise tie-lines requires significant experimental effort. In this study, a machine learning approach was used to predict the Tm of lipids and generate phase diagrams from lipid mixtures. First, artificial neural network (ANN) was used for the prediction of Tm. The network was trained using available Tm data and was able to generate Tm values that closely matched literature results for its testing dataset. This model was then used to predict the Tm for lipids that have not yet been experimentally tested. Then, random forests (RF) and support vector machines (SVM) were trained and tested for their ability to predict a test three-component phase diagram. The model from the RF algorithm was able to generate a diagram that closely matched published results. This model was then used to generate phase diagrams for lipid mixtures at various temperatures and various degrees of unsaturation. This machine learning approach to the generation of lipid phase diagrams has the potential to save significant time and resources in studies of lipid phase behavior.