A study of the dynamic relation between physiological changes and spontaneous expressions

Recent progress in Affective Computing (AC) has enabled integration of physiological cues and spontaneous expressions to reveal a subject’s emotional state. Due to the lack of an effective technique for evaluating multimodal correlations, experience and intuition play a main role in present AC studies when fusing affective cues or modalities, resulting in unexpected outcomes. This study seeks to demonstrate a dynamic correlation between two such affective cues, physiological changes and spontaneous expressions, which were obtained by a combination of stereo vision based tracking and imaging photoplethysmography (iPPG), with a designed protocol involving 20 healthy subjects. The two cues obtained were sampled into a Statistical Association Space (SAS) to evaluate their dynamic correlation. It is found that the probability densities in the SAS increase as the peaks in two cues are approached. Also the complex form of the high probability density region in the SAS suggests a nonlinear correlation between two cues. Finally the cumulative distribution on the zero time-difference surface is found to be small (<0.047) demonstrating a lack of simultaneity. These results show that the two cues have a close interrelation, that is both asynchronous and nonlinear, in which a peak of one cue heralds a peak in the other

Transmembrane Permeation Mechanism of Charged Methyl Guanidine

The mechanism of transmembrane ion permeation is studied using charged methyl guanidine as a model ion. With a widely applied reaction coordinate, our umbrella sampling results reveal a significant finite-size effect in small simulation systems and a serious hysteresis in large systems. Therefore, it is important to re-examine the simulation techniques for studying transmembrane permeation mechanism of ions suggested in previous works. In this work, two novel collective variables are designed to acquire a continuous trajectory of the permeation process and small statistical errors through umbrella sampling. A water-bridge mechanism is discussed in detail. In this mechanism, a continuous water chain (or a chain of water molecules and lipid head groups) is formed across the membrane to conduct the transmembrane permeation of charged methyl guanidine. We obtain a continuous transition trajectory by combining the two-dimensional umbrella sampling in the local region of the saddle state and a one-dimensional sampling in the out region. Our free energy analysis shows that, with the presence of the water bridge, the energy barrier of the transmembrane permeation of ions is reduced significantly. Our analysis suggests that the water-bridge mechanism is common for permeation of ions across thick membranes, including palmitoyloleoyl phosphocholine and dipalmitoylphosphatidylcholine membranes

A new engineering approach to reveal correlation of physiological change and spontaneous expression from video images

Spontaneous expression is associated with physiological states, i.e., heart rate, respiration, oxygen saturation (SpO2%), and heart rate variability (HRV). There have yet not sufficient efforts to explore correlation of physiological change and spontaneous expression. This study aims to study how spontaneous expression is associated with physiological changes with an approved protocol or through the videos provided from Denver Intensity of Spontaneous Facial Action Database. Not like a posed expression, motion artefact in spontaneous expression is one of evitable challenges to be overcome in the study. To obtain a physiological signs from a region of interest (ROI), a new engineering approach is being developed with an artefact-reduction method consolidated 3D active appearance model (AAM) based track, affine transformation based alignment with opto-physiological mode based imaging photoplethysmography. Also, a statistical association spaces is being used to interpret correlation of spontaneous expressions and physiological states including their probability densities by means of Gaussian Mixture Model. The present work is revealing a new avenue of study associations of spontaneous expressions and physiological states with its prospect of applications on physiological and psychological assessment

Impact of Resistance Mutations on Inhibitor Binding to HIV‑1 Integrase

HIV-1 integrase (IN) is essential for HIV-1 replication, catalyzing two key reaction steps termed 3′ processing and strand transfer. Therefore, IN has become an important target for antiviral drug discovery. However, mutants have emerged, such as E92Q/N155H and G140S/Q148H, which confer resistance to raltegravir (RAL), the first IN strand transfer inhibitor (INSTI) approved by the FDA, and to the recently approved elvitegravir (EVG). To gain insights into the molecular mechanisms of ligand binding and drug resistance, we performed molecular dynamics (MD) simulations of homology models of the HIV-1 IN and four relevant mutants complexed with viral DNA and RAL. The results show that the structure and dynamics of the 140s’ loop, comprising residues 140 to 149, are strongly influenced by the IN mutations. In the simulation of the G140S/Q148H double mutant, we observe spontaneous dissociation of RAL from the active site, followed by an intrahelical swing-back of the 3′-OH group of nucleotide A17, consistent with the experimental observation that the G140S/Q148H mutant exhibits the highest resistance to RAL compared to other IN mutants. An important hydrogen bond between residues 145 and 148 is present in the wild-type IN but not in the G140S/Q148H mutant, accounting for the structural and dynamical differences of the 140s’ loop and ultimately impairing RAL binding in the double mutant. End-point free energy calculations that broadly capture the experimentally known RAL binding profiles elucidate the contributions of the 140s’ loop to RAL binding free energies and suggest possible approaches to overcoming drug resistance

Additional file 1 of Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

Additional file 1: Supplementary Figure 1. GO Enrichment pathway associated with cellular component, and biological process. Supplementary Figure 2. The differentially expression of 10 plasma protein candidates among three groups. Supplementary Figure 3. The differentially expression of 14 serum amino acids among three groups. Supplementary Figure 4. The differentially expression of 15 bile acids among three groups. Supplementary Figure 5. The differentially expression of six classic tumor markers among three groups. Supplementary Figure 6. Proteins and amnio acids related to NSCLC stage. Supplementary Figure 7. Single index with AUC>0.7 for NSCLC screening. Supplementary Figure 8. Single index with AUC>0.7 in differentiating NSCLC and BPD. Supplementary Figure 9.The process and the result of binary logistic regression with backward elimination methods. Supplementary Table 1. Screened differentially expressed proteins and corresponding validation proteins. Supplementary Table 2. Performance of single predictor in NSCLC screening. Supplementary Table 3. Performance of single predictor in NSCLC diagnosis. Supplementary Table 4. Screening model by stepwise binary logistic regression analysis in training samples. Supplementary Table 5. Performance analysis of 3 models in screening NSCLC. Supplementary Table 6. Testing of 3 models in screening NSCLC. Supplementary Table 7. Diagnosis model by stepwise binary logistic regression analysis in training samples. Supplementary Table 8. Performance analysis of 3 models in differentiating NSCLC and BPD. Supplementary Table 9. Testing of 3 models in differentiating NSCLC and BPD. Supplementary Table 10. The concentration units of these candidates