The room temperature pump-probe X-ray free electron laser (XFEL) measurements used for serial femtosecond crystallography provide remarkable information about the structures of the catalytic (S-state) intermediates of the oxygen-evolution reaction of photosystem II. However, mixed populations of these intermediates and moderate resolution limit the interpretation of the data from current experiments. The S3 XFEL structures show extra density near the OEC that may correspond to a water/hydroxide molecule. However, in the latest structure, this additional oxygen is 2.08 Å from the Oe2 of D1-E189, which is closer than the sum of the van der Waals radii of the two oxygens. Here, we use Boltzmann statistics and Monte Carlo sampling to provide a model for the S2-to-S3 state transition, allowing structural changes and the insertion of an additional water/hydroxide. Based on our model, water/hydroxide addition to the oxygenevolving complex (OEC) is not thermodynamically favorable in the S2 g = 2 state, but it is in the S2 g = 4.1 redox isomer. Thus, formation of the S3 state starts by a transition from the S2 g = 2 to the S2 g = 4.1 structure. Then, electrostatic interactions support protonation of D1-H190 and deprotonation of the Ca 2+ -ligated water (W3) with proton loss to the lumen. The W3 hydroxide moves toward Mn4, completing the coordination shell of Mn4 and moving with its oxidation to Mn(IV) in the S3 state. In addition, binding additional hydroxide to Mn1 leads to a conformational change of D1-E189 in the S2 g = 4.1 and S3 structures. In the S3 state in the population of protonated D1-E189 increases.
Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills.
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