Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes: BioVRSea paradigm.

Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task. Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses. Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML). Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.

Hydrogen Bond Networks in Binary Mixtures of Water and Organic Solvents.

We here present an approach for the optical in situ characterization of hydrogen bond networks (HBNs) in binary mixtures of water and organic solvents (OSs), such as methanol, ethanol, and acetonitrile. HBNs are characterized based on (i) the analysis of experimental molar Raman spectra of the mixture, (ii) partial molar Raman spectra of the mixture constituents, and (iii) computed ideal molar Raman spectra of the mixture. Especially, the consideration of the partial molar Raman spectra provides insights into the development of hydrogen bonds of molecules of one species with their neighbors. The obtained Raman spectra are evaluated with respect to the centroid of the symmetric stretching vibration Raman signal of water and to the hydroxyl stretching vibration of alcohols. We show the influence of composition and temperature on the development of the HBN of the mixtures, the HBN of water, and the HBN of the OS molecules.

Pressure-Responsive, Surfactant-Free CO2-Based Nanostructured Fluids.

Microemulsions are extensively used in advanced material and chemical processing. However, considerable amounts of surfactant are needed for their formulation, which is a drawback due to both economic and ecological reasons. Here, we describe the nanostructuration of recently discovered surfactant-free, carbon dioxide (CO2)-based microemulsion-like systems in a water/organic-solvent/CO2 pressurized ternary mixture. "Water-rich" nanodomains embedded into a "water-depleted" matrix have been observed and characterized by the combination of Raman spectroscopy, molecular dynamics simulations, and small-angle neutron scattering. These single-phase fluids show a reversible, pressure-responsive nanostructuration; the "water-rich" nanodomains at a given pressure can be instantaneously degraded/expanded by increasing/decreasing the pressure, resulting in a reversible, rapid, and homogeneous mixing/demixing of their content. This pressure-triggered responsiveness, together with other inherent features of these fluids, such as the absence of any contaminant in the ternary mixture (e.g., surfactant), their spontaneous formation, and their solvation capability (enabling the dissolution of both hydrophobic and hydrophilic molecules), make them appealing complex fluid systems to be used in molecular material processing and in chemical engineering.

Determination of vapor-liquid equilibrium data in microfluidic segmented flows at elevated pressures using Raman spectroscopy.

A fast, noninvasive, and efficient analytical measurement strategy for the characterization of vapor-liquid equilibria (VLE) is presented, which is based on phase (state of matter) selective Raman spectroscopy in multiphase flows inside microcapillay systems (MCS). Isothermal VLE data were measured in binary and ternary mixtures composed of acetone, water, carbon dioxide or nitrogen at elevated pressures up to 10 MPa and temperatures up to 333 K. For validation, the obtained data were compared with literature data and reference measurements in a high-pressure variable volume cell. Additionally, the mixtures were investigated at temperatures and pressures where no data is available in literature to extend the high-pressure VLE database.