Epicatechin content and antioxidant capacity of cocoa beans from four different countries

Natural antioxidant has received more attention to be part of daily diet. Cocoa beans is one of the main sources of polyphenols especially epicatechin. This study was conducted to investigate the relationship between antioxidant potential and epicatechin content of raw cocoa beans from different countries, namely Malaysia, Ghana, Cote d’Ivoire and Sulawesi (Indonesia). Antioxidant potential was determined using trolox-equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP) assays. Reversed-phase high performance liquid chromatography (HPLC) was used to quantify the amount of epicatechin. The epicatechin content of raw cocoa beans was in the range of 270 - 1235mg/100 g cocoa beans. Based on the two assays, Sulawesian beans exhibited the highest antioxidant capacity followed by Malaysian, Ghanaian and Cote d’Ivoirian beans for both extracts. Both ethanolic (r= 0.92) and water (r = 0.90) extracts of cocoa beans showed a significant positive and high correlation between epicatechin and TEAC value. Similarly, FRAP assay also showed a positive and high correlation with epicatechin for both ethanolic (r = 0.84) and water (r = 0.79) extracts. Results indicatedthat antioxidant capacity using two different antioxidant assays exhibited a positive and high correlation with epicatechin content in cocoa beans. Thus, epicatechin content in cocoa beans could be responsible for the antioxidant capacity

Barriers effecting physical activity: Empirical study of middle aged staffs

A broad understanding of the barriers inhibiting physical activity among middle aged is critical because it affect productivity at workplace. Research stream on barriers effecting physical activity is sparse, hence this paper aims to fill the void by assessing the barriers for middle-aged staffs in participating physical activity which at a minimum recommended amount to maintain health and function.This study employed Quantitative survey method by combining a set of International Physical Activity Questionnaire (IPAQ) and Barriers Questionnaire adopted from Australian Bureau of Statistics.The participants in this cross-sectional study were 225 middle-aged Custom staff age 35 to 55 years old and randomly selected. Evidence established that the barriers to participate in physical activity among middle-aged custom employees were both effected by the internal (lacks of energy) and external (lack of time) factors. Discussions of the implication for future directions were deliberated

Professional development and sustainable development goals

Professional development is defined as a consciously designed systematic process that helps professionals to attain, utilize, and retain knowledge, skills, and expertise. It is simply a process of obtaining skills, qualifications, and experience that help in advancement in one’s career. In the field of education, it is defined as the process of improving staff skills and competencies needed to produce outstanding performance of students. It also refers to a process of improving an organization’s staff capabilities through access to education and training opportunities for better output. Professional development can include a variety of approaches such as formal and informal education, vocational, specialized, or skill-based training, or advanced professional learning

Suppression of PGE2 production via disruption of MAPK phosphorylation by unsymmetrical dicarbonyl curcumin derivatives

Curcumin is an important molecule found in turmeric plants and has been reported to exhibit some profound anti-inflammatory activities by interacting with several important molecular targets found in the mitogen-activated protein kinase and NF-κβ pathways. As part of our continuing effort to search for new anti-inflammatory agents with better in vitro and in vivo efficacies, we have synthesized a series of new unsymmetrical dicarbonyl curcumin derivatives and tested their effects on prostaglandin E2 secretion level in interferon-γ/lipopolysaccharide-activated macrophage cells. Among those, five compounds exhibited remarkable suppression on prostaglandin E2 production with IC50 values ranging from 0.87 to 18.41 µM. The most potent compound 17f was found to down-regulate the expression of cyclooxygenase-2 mRNA suggesting that this series of compounds could possibly target the mitogen-activated protein kinase signal transduction pathway. Whilst the compound did not affect the expression of the conventional mitogen-activated protein kinases, the results suggest that it could disrupt the phosphorylation and activation of the proteins particularly the c-Jun N-terminal kinases. Finally, the binding interactions were examined using the molecular docking and dynamics simulation approaches

Resveratrol regulates neuro-inflammation and induces adaptive immunity in Alzheimer’s disease

BACKGROUND: Treatment of mild-moderate Alzheimer’s disease (AD) subjects (N = 119) for 52 weeks with the SIRT1 activator resveratrol (up to 1 g by mouth twice daily) attenuates progressive declines in CSF Aβ40 levels and activities of daily living (ADL) scores. METHODS: For this retrospective study, we examined banked CSF and plasma samples from a subset of AD subjects with CSF Aβ42 <600 ng/ml (biomarker-confirmed AD) at baseline (N = 19 resveratrol-treated and N = 19 placebo-treated). We utilized multiplex Xmap technology to measure markers of neurodegenerative disease and metalloproteinases (MMPs) in parallel in CSF and plasma samples. RESULTS: Compared to the placebo-treated group, at 52 weeks, resveratrol markedly reduced CSF MMP9 and increased macrophage-derived chemokine (MDC), interleukin (IL)-4, and fibroblast growth factor (FGF)-2. Compared to baseline, resveratrol increased plasma MMP10 and decreased IL-12P40, IL12P70, and RANTES. In this subset analysis, resveratrol treatment attenuated declines in mini-mental status examination (MMSE) scores, change in ADL (ADCS-ADL) scores, and CSF Aβ42 levels during the 52-week trial, but did not alter tau levels. CONCLUSIONS: Collectively, these data suggest that resveratrol decreases CSF MMP9, modulates neuro-inflammation, and induces adaptive immunity. SIRT1 activation may be a viable target for treatment or prevention of neurodegenerative disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT0150485

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. Stud. 59, 55–64 (2003).Jerritta, S., Murugappan, M., Nagarajan, R. & Wan, K. Physiological signals based human emotion Recognition: a review. Signal Process. its Appl. (CSPA), 2011 IEEE 7th Int. Colloq. 410–415, https://doi.org/10.1109/CSPA.2011.5759912 (2011).Harms, M. B., Martin, A. & Wallace, G. L. Facial emotion recognition in autism spectrum disorders: A review of behavioral and neuroimaging studies. Neuropsychol. Rev. 20, 290–322 (2010).Koolagudi, S. G. & Rao, K. S. Emotion recognition from speech: A review. Int. J. Speech Technol. 15, 99–117 (2012).Gross, J. J. & Levenson, R. W. Emotion elicitation using films. Cogn. Emot. 9, 87–108 (1995).Lindal, P. J. & Hartig, T. Architectural variation, building height, and the restorative quality of urban residential streetscapes. J. Environ. Psychol. 33, 26–36 (2013).Ulrich, R. View through a window may influence recovery from surgery. Science (80-.). 224, 420–421 (1984).Fernández-Caballero, A. et al. Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 64, 55–73 (2016).Ekman, P. Basic Emotions. Handbook of cognition and emotion 45–60, https://doi.org/10.1017/S0140525X0800349X (1999).Posner, J., Russell, J. A. & Peterson, B. S. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–34 (2005).Russell, J. A. & Mehrabian, A. Evidence for a three-factor theory of emotions. J. Res. Pers. 11, 273–294 (1977).Calvo, R. A. & D’Mello, S. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1, 18–37 (2010).Valenza, G. et al. Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150176 (2016).Valenza, G., Lanata, A. & Scilingo, E. P. The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans. Affect. Comput. 3, 237–249 (2012).Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P. & Barbieri, R. Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics. Sci. Rep. 4, 4998 (2014).Valenza, G. et al. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Heal. Informatics 18, 1625–1635 (2014).Piwek, L., Ellis, D. A., Andrews, S. & Joinson, A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med. 13, 1–9 (2016).Xu, J., Mitra, S., Van Hoof, C., Yazicioglu, R. & Makinwa, K. A. A. Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology. IEEE Rev. Biomed. Eng. 3333, 1–1 (2017).Kumari, P., Mathew, L. & Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 90, 298–307 (2017).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds Yang, C., Virk, G. S. & Yang, H.) 15–25. https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017).Nakisa, B., Rastgoo, M. N., Tjondronegoro, D. & Chandran, V. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst. Appl. 93, 143–155 (2018).Kory Jacqueline, D. & Sidney, K. Affect Elicitation for Affective Computing. In The Oxford Handbook of Affective Computing 371–383 (2014).Ekman, P. The directed facial action task. In Handbook of emotion elicitation and assessment 47–53 (2007).Harmon-Jones, E., Amodio, D. M. & Zinner, L. R. Social psychological methods of emotion elicitation. Handb. Emot. elicitation Assess. 91–105, https://doi.org/10.2224/sbp.2007.35.7.863 (2007)Roberts, N. A., Tsai, J. L. & Coan, J. A. Emotion elicitation using dyadic interaction task. Handbook of Emotion Elicitation and Assessment 106–123 (2007).Nardelli, M., Valenza, G., Greco, A., Lanata, A. & Scilingo, E. P. Recognizing emotions induced by affective sounds through heart rate variability. IEEE Trans. Affect. Comput. 6, 385–394 (2015).Kim, J. Emotion Recognition Using Speech and Physiological Changes. Robust Speech Recognit. Underst. 265–280 (2007).Soleymani, M., Pantic, M. & Pun, T. Multimodal emotion recognition in response to videos (Extended abstract). 2015 Int. Conf. Affect. Comput. Intell. Interact. ACII 2015 3, 491–497 (2015).Baños, R. M. et al. Immersion and Emotion: Their Impact on the Sense of Presence. CyberPsychology Behav. 7, 734–741 (2004).Giglioli, I. A. C., Pravettoni, G., Martín, D. L. S., Parra, E. & Raya, M. A. A novel integrating virtual reality approach for the assessment of the attachment behavioral system. Front. Psychol. 8, 1–7 (2017).Marín-Morales, J., Torrecilla, C., Guixeres, J. & Llinares, C. Methodological bases for a new platform for the measurement of human behaviour in virtual environments. DYNA 92, 34–38 (2017).Vince, J. Introduction to virtual reality. (Media, Springer Science & Business, 2004).Alcañiz, M., Baños, R., Botella, C. & Rey, B. The EMMA Project: Emotions as a Determinant of Presence. PsychNology J. 1, 141–150 (2003).Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Slater, M. & Wilbur, S. A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence Teleoperators Virtual Environ. 6, 603–616 (1997).Riva, G. et al. Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology Behav. 10, 45–56 (2007).Baños, R. M. et al Changing induced moods via virtual reality. In International Conference on Persuasive Technology (ed. Springer, Berlin, H.) 7–15, https://doi.org/10.1007/11755494_3 (2006).Baños, R. M. et al. Positive mood induction procedures for virtual environments designed for elderly people. Interact. Comput. 24, 131–138 (2012).Gorini, A. et al. Emotional Response to Virtual Reality Exposure across Different Cultures: The Role of the AttributionProcess. CyberPsychology Behav. 12, 699–705 (2009).Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F. & Riva, G. The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behav. Soc. Netw. 14, 99–105 (2011).Chirico, A. et al. Effectiveness of Immersive Videos in Inducing Awe: An Experimental Study. Sci. Rep. 7, 1–11 (2017).Blascovich, J. et al. Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychol. Inq. 7965, 103–124 (2012).Peperkorn, H. M., Alpers, G. W. & Mühlberger, A. Triggers of fear: Perceptual cues versus conceptual information in spider phobia. J. Clin. Psychol. 70, 704–714 (2014).McCall, C., Hildebrandt, L. K., Bornemann, B. & Singer, T. Physiophenomenology in retrospect: Memory reliably reflects physiological arousal during a prior threatening experience. Conscious. Cogn. 38, 60–70 (2015).Hildebrandt, L. K., Mccall, C., Engen, H. G. & Singer, T. Cognitive flexibility, heart rate variability, and resilience predict fine-grained regulation of arousal during prolonged threat. Psychophysiology 53, 880–890 (2016).Notzon, S. et al. Psychophysiological effects of an iTBS modulated virtual reality challenge including participants with spider phobia. Biol. Psychol. 112, 66–76 (2015).Amaral, C. P., Simões, M. A., Mouga, S., Andrade, J. & Castelo-Branco, M. A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study. J. Neurosci. Methods 290, 105–115 (2017).Eudave, L. & Valencia, M. Physiological response while driving in an immersive virtual environment. 2017 IEEE 14th Int. Conf. Wearable Implant. Body Sens. Networks 145–148, https://doi.org/10.1109/BSN.2017.7936028 (2017).Sharma, G. et al. Influence of landmarks on wayfinding and brain connectivity in immersive virtual reality environment. Front. Psychol. 8, 1–12 (2017).Bian, Y. et al. A framework for physiological indicators of flow in VR games: construction and preliminary evaluation. Pers. Ubiquitous Comput. 20, 821–832 (2016).Egan, D. et al. An evaluation of Heart Rate and Electrodermal Activity as an Objective QoE Evaluation method for Immersive Virtual Reality Environments. 3–8, https://doi.org/10.1109/QoMEX.2016.7498964 (2016).Meehan, M., Razzaque, S., Insko, B., Whitton, M. & Brooks, F. P. Review of four studies on the use of physiological reaction as a measure of presence in stressful virtual environments. Appl. Psychophysiol. Biofeedback 30, 239–258 (2005).Higuera-Trujillo, J. L., López-Tarruella Maldonado, J. & Llinares Millán, C. Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality. Appl. Ergon. 65, 398–409 (2016).Felnhofer, A. et al. Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios. Int. J. Hum. Comput. Stud. 82, 48–56 (2015).Anderson, A. P. et al. Relaxation with Immersive Natural Scenes Presented Using Virtual Reality. Aerosp. Med. Hum. Perform. 88, 520–526 (2017).Higuera, J. L. et al. Emotional cartography in design: A novel technique to represent emotional states altered by spaces. In D and E 2016: 10th International Conference on Design and Emotion 561–566 (2016).Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).Bradley, M. M. & Lang, P. J. Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59 (1994).Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International Affective Picture System (IAPS): Technical Manual and Affective Ratings. NIMH Cent. Study Emot. Atten. 39–58, https://doi.org/10.1027/0269-8803/a000147 (1997).Nanda, U., Pati, D., Ghamari, H. & Bajema, R. Lessons from neuroscience: form follows function, emotions follow form. Intell. Build. Int. 5, 61–78 (2013).Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).Sejima, K. Kazuyo Sejima. 1988–1996. El Croquis 15 (1996).Ochiai, H. et al. Physiological and Psychological Effects of Forest Therapy on Middle-Aged Males with High-NormalBlood Pressure. Int. J. Environ. Res. Public Health 12, 2532–2542 (2015).Noguchi, H. & Sakaguchi, T. Effect of illuminance and color temperature on lowering of physiological activity. Appl. Hum. Sci. 18, 117–123 (1999).Küller, R., Mikellides, B. & Janssens, J. Color, arousal, and performance—A comparison of three experiments. Color Res. Appl. 34, 141–152 (2009).Yildirim, K., Hidayetoglu, M. L. & Capanoglu, A. Effects of interior colors on mood and preference: comparisons of two living rooms. Percept. Mot. Skills 112, 509–524 (2011).Hogg, J., Goodman, S., Porter, T., Mikellides, B. & Preddy, D. E. Dimensions and determinants of judgements of colour samples and a simulated interior space by architects and non‐architects. Br. J. Psychol. 70, 231–242 (1979).Jalil, N. A., Yunus, R. M. & Said, N. S. Environmental Colour Impact upon Human Behaviour: A Review. Procedia - Soc. Behav. Sci. 35, 54–62 (2012).Jacobs, K. W. & Hustmyer, F. E. Effects of four psychological primary colors on GSR, heart rate and respiration rate. Percept. Mot. Skills 38, 763–766 (1974).Jin, H. R., Yu, M., Kim, D. W., Kim, N. G. & Chung, A. S. W. Study on Physiological Responses to Color Stimulation. In International Association of Societies of Design Research (ed. Poggenpohl, S.) 1969–1979 (Korean Society of Design Science, 2009).Vartanian, O. et al. Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proc. Natl. Acad. Sci. 110, 1–8 (2013).Tsunetsugu, Y., Miyazaki, Y. & Sato, H. Visual effects of interior design in actual-size living rooms on physiological responses. Build. Environ. 40, 1341–1346 (2005).Stamps, A. E. Physical Determinants of Preferences for Residential Facades. Environ. Behav. 31, 723–751 (1999).Berlyne, D. E. Novelty, Complexity, and Hedonic Value. Percept. Psychophys. 8, 279–286 (1970).Krueger, R. A. & Casey, M. Focus groups: a practical guide for applied research. (Sage Publications, 2000).Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M. & Suri, J. S. Heart rate variability: A review. Med. Biol. Eng. Comput. 44, 1031–1051 (2006).Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-aho, P. O. & Karjalainen, P. A. Kubios HRV - Heart rate variability analysis software. Comput. Methods Programs Biomed. 113, 210–220 (2014).Pan, J. & Tompkins, W. J. A real-time QRS detection algorithm. Biomed. Eng. IEEE Trans. 1, 230–236 (1985).Tarvainen, M. P., Ranta-aho, P. O. & Karjalainen, P. A. An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49, 172–175 (2002).Valenza, G. et al. Predicting Mood Changes in Bipolar Disorder Through HeartbeatNonlinear Dynamics. IEEE J. Biomed. Heal. Informatics 20, 1034–1043 (2016).Pincus, S. & Viscarello, R. Approximate Entropy A regularity measure for fetal heart rate analysis. Obstet. Gynecol. 79, 249–255 (1992).Richman, J. & Moorman, J. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Hear. Circ Physiol 278, H2039–H2049 (2000).Peng, C.-K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995).Grassberger, P. & Procaccia, I. Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983).Delorme, A. & Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).Colomer Granero, A. et al. A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Front. Comput. Neurosci. 10, 1–14 (2016).Kober, S. E., Kurzmann, J. & Neuper, C. Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study. Int. J. Psychophysiol. 83, 365–374 (2012).Hyvärinen, A. & Oja, E. Independent component analysis: Algorithms and applications. Neural Networks 13, 411–430 (2000).Welch, P. D. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Aver. aging Over Short, Modified Periodograms. IEEE Trans. AUDIO Electroacoust. 15, 70–73 (1967).Mormann, F., Lehnertz, K., David, P. & Elger, E. C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys. D Nonlinear Phenom. 144, 358–369 (2000).Jolliffe, I. T. Principal Component Analysis, Second Edition. Encycl. Stat. Behav. Sci. 30, 487 (2002).Schöllkopf, B., Smola, A. J., Williamson, R. C. & Bartlett, P. L. New support vector algorithms. Neural Comput 12, 1207–1245 (2000).Yan, K. & Zhang, D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors Actuators, B Chem. 212, 353–363 (2015).Chang, C.-C. & Lin, C.-J. Libsvm: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).Lewis, P. A., Critchley, H. D., Rotshtein, P. & Dolan, R. J. Neural correlates of processing valence and arousal in affective words. Cereb. Cortex 17, 742–748 (2007).McCall, C., Hildebrandt, L. K., Hartmann, R., Baczkowski, B. M. & Singer, T. Introducing the Wunderkammer as a tool for emotion research: Unconstrained gaze and movement patterns in three emotionally evocative virtual worlds. Comput. Human Behav. 59, 93–107 (2016).Blake, J. & Gurocak, H. B. Haptic glove with MR brakes for virtual reality. IEEE/ASME Trans. Mechatronics 14, 606–615 (2009).Heydarian, A. et al. Immersive virtual environments versus physical built environments: A benchmarking study for building design and user-built environment explorations. Autom. Constr. 54, 116–126 (2015).Kuliga, S. F., Thrash, T., Dalton, R. C. & Hölscher, C. Virtual reality as an empirical research tool - Exploring user experience in a real building and a corresponding virtual model. Comput. Environ. Urban Syst. 54, 363–375 (2015).Yeom, D., Choi, J.-H. & Zhu, Y. Investigation of the Physiological Differences between Immersive Virtual Environment and Indoor Enviorment in a Building. Indoor adn Built Enviornment 0, Accept (2017).Combrisson, E. & Jerbi, K. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds. Yang, C., Virk, G. S. & Yang, H.) 15–25, https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017)

Abundant interaction between lump and k-kink, periodic and other analytical solutions for the (3+1)-D Burger system by bilinear analysis

In this paper, we study the (3+1)-dimensional Burger system which is considered in soliton theory and generated by considering the Hirota bilinear operators. The bilinear frame to the Burger system by using the multi-dimensional Bell polynomials is constructed. Also, based on the binary Backlund transformations, the generalized Bell polynomials are written. We retrieve some novel exact analytical solutions, containing interaction between lump and two kink wave solutions, interaction between lump and periodic wave solutions, interaction between stripe and periodic solutions, breather wave solutions, cross-kink wave solutions, interaction between kink and periodic wave solutions, multi-wave solutions, and finally solitary wave solutions for the (3+1)-dimensional Burger system by Maple symbolic computations. The required conditions of the analyticity and positivity of the solutions can be easily achieved by taking special choices of the involved parameters. The main ingredients for this scheme are to recover the Hirota trilinear forms and their generalized equivalences

Bariatric Surgery in Patients with Cirrhosis: Careful Patient and Surgery-Type Selection Is Key to Improving Outcomes

Previous reports suggest an increased mortality in cirrhotic patients undergoing bariatric surgery (BS). With advancements in management of BS, we aim to study the trends, outcomes, and their predictors in patients with cirrhosis undergoing BS. A retrospective study was performed using the National Database from 2008 to 2013. Outcomes of BS in patients with cirrhosis were studied. In-hospital mortality, length of stay, and cost of care were compared between patients with no cirrhosis (NC), compensated cirrhosis (CC), and decompensated cirrhosis (DC). Multivariable logistic regression analysis was performed to study the predictors of mortality. Of the 558,017 admissions of patients who underwent BS during the study period, 3086 (0.55%) had CC and 103 (0.02%) had DC. An upward trend of vertical sleeve gastrectomy (VSG) utilization was seen during the study period. On multivariate analysis, mortality in CC was comparable with those in NC (aOR 1.88; CI 0.65-5.46); however, it was higher in DC (aOR 83.8; CI 19.3-363.8). Other predictors of mortality were older age (aOR 1.06; CI 1.04-1.08), male (aOR 2.59; CI 1.76-3.81), Medicare insurance (aOR 1.93; CI 1.24-3.01), lower income (aORs 0.44 to 0.55 for 2nd to 4th income quartile vs. 1st quartile), > 3 Elixhauser Comorbidity Index (aOR 5.30; CI 3.45-8.15), undergoing Roux-en-Y gastric bypass as opposed to VSG (aOR 3.90; CI 1.79-8.48), and centers performing < 50 BS per year (aOR 5.25; CI 3.38-8.15). Length of stay and hospital cost were also significantly higher in patients with cirrhosis as compared with those with NC. Patients with compensated cirrhosis can be considered for bariatric surgery. However, careful selection of patients, procedure type, and volume of surgical center is integral in improving outcomes and healthcare utilization in patients with cirrhosis undergoing BS