Mn 2+ reduces Y z + in manganese-depleted Photosystem II preparations

Manganese in the oxygen-evolving complex is a physiological electron donor to Photosystem II. PS II depleted of manganese may oxidize exogenous reductants including benzidine and Mn 2+ . Using flash photolysis with electron spin resonance detection, we examined the room-temperature reaction kinetics of these reductants with Y z + , the tyrosine radical formed in PS II membranes under illumination. Kinetics were measured with membranes that did or did not contain the 33 kDa extrinsic polypeptide of PS II, whose presence had no effect on the reaction kinetics with either reductant. The rate of Y z + reduction by benzidine was a linear function of benzidine concentration. The rate of Y z + reduction by Mn 2+ at pH 6 increased linearly at low Mn 2+ concentrations and reached a maximum at the Mn 2+ concentrations equal to several times the reaction center concentration. The rate was inhibited by K + , Ca 2+ and Mg 2+ . These data are described by a model in which negative charge on the membrane causes a local increase in the cation concentration. The rate of Y z + reduction at pH 7.5 was biphasic with a fast 400 μs phase that suggests binding of Mn 2+ near Y z + at a site that may be one of the native manganese binding sites.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43534/1/11120_2004_Article_BF00048306.pd

Detecting and influencing driver emotions using psycho-physiological sensors and ambient light

Driving is a sensitive task that is strongly affected by the driver's emotions. Negative emotions, such as anger, can evidently lead to more driving errors. In this work, we introduce a concept of detecting and influencing driver emotions using psycho-physiological sensing for emotion classification and ambient light for feedback. We detect arousal and valence of emotional responses from wearable bio-electric sensors, namely brain-computer interfaces and heart rate sensors. We evaluated our concept in a static driving simulator with a fully equipped car with 12 participants. Before the rides, we elicit negative emotions and evaluate driving performance and physiological data while driving under stressful conditions. We use three ambient lighting conditions (no light, blue, orange). Using a subject-dependent random forests classifier with 40 features collected from physiological data we achieve an average accuracy of 78.9% for classifying valence and 68.7% for arousal. Driving performance was enhanced in conditions where ambient lighting was introduced. Both blue and orange light helped drivers to improve lane keeping. We discuss insights from our study and provide design recommendations for designing emotion sensing and feedback systems in the car