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  • Participants were asked to sit as still as possible

    2018-10-29

    Participants were asked to sit as still as possible, to blink as little as possible, and to rest their chin on a chinrest to ensure a constant distance to the computer (57cm). Participants were instructed to ignore the sounds.
    EEG recording EEG data were recorded from six electrodes at standard 10/20 positions (Fpz, Fz, Cz, M1, M2, and tip of nose) with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands). Fpz, Fz, and Cz were recorded with pin electrodes in a 64-electrode EEG cap; and M1, M2, and the tip of the nose were recorded with flat electrodes attached with adhesive disks. Two additional, system-specific electrodes were recorded with pin electrodes in the EEG cap: the CMS (between PO3 and POz) served as the internal reference electrode, and DRL (between POz and PO4) as the ground electrode. Data were sampled at 512Hz and filtered with a hardware low-pass filter at 104Hz. No high-pass filter was used. All physiological data were processed offline using the FieldTrip toolbox in MATLAB [4]. Continuous data were re-referenced to the tip of the nose.
    Data analysis For each participant, ERPs were computed for correct rejections (i.e., only trials that did not require a behavioral response and did not evoke a response), separately for each load. Epochs were extracted from 100ms before tone onset to 400ms after. Each pyk2 inhibitor was baseline corrected with the 100-ms interval before tone onset. For each participant, amplitude ranges (i.e., max minus min) within individual epochs were extracted and visually inspected to exclude apparent outliers. Cutoffs were adjusted individually to retain as many trials as possible while reducing the potential effects of outliers (amplitude ranges did not exceed 120µV). Inspection was conducted by a researcher who was blind to the condition (stimulus and load) of individual trials, as well as blind to the SPL of each participant. Ten participants were excluded because they had less than 50% remaining trials. Fig. 1 shows grand mean waveforms of standards and deviants recorded from the different electrodes at Fz, Cz, M1, and M2. To identify the MMN, a difference wave was computed for each participant by subtracting the mean ERP to standards from that to deviants across both load conditions. Across subjects, there was an apparent negativity at the frontal electrodes and a polarity reversal at the mastoids between 160 and 220ms after tone onset. For Gaia interval (160–220ms), mean amplitudes were extracted for Fz, Cz, and mastoids for each condition. Fig. 2 shows the grand mean waveforms of the MMN (i.e., deviant minus standard) for each load at Fz and Cz (left column), the difference wave in MMN between loads (middle column), and a scatterplot of the mean MMN amplitudes between low and high load for the three SPLs. For the actual analyses, only the MMN amplitudes at Fz were used because this electrode is used most often [2,5], and is recommended by guidelines [6]. In the processing of the behavioral data, responses faster than 200ms were excluded. Because the task had a rapid pace (ITI of 1s), we were concerned that responses faster than 200ms may be late responses to the previous trial. Hit rates and false alarm rates were computed for each condition (i.e., tone deviance by load). Signal detection analyses were performed to compute d′ [7]. To avoid floor and ceiling effects on hit and false alarm rates, we added 0.5 trial in the numerator and 1 trial in the denominator [8].
    Acknowledgments Funded by internal funds from Stockholm University and a research grant from the Swedish Research Council (2015-01181) to Stefan Wiens. We thank Joanna Lindström for proofreading.
    Data
    Experimental design, materials and methods
    Acknowledgements This work was supported by Grants-in-Aid both for Scientific Research from the Japanese MEXT and from the Japanese MHLW.
    1. Data Two strains of Acinetobacter sp. isolated from rice rhizosphere, AGM3 (Acinetobacter sp; NCBI Accession Number-KP888315), AGM9 (Acinetobacter sp; NCBI Accession Number – KP888316), reported earlier by us as potential zinc solubilizer [1]. The dataset of this article contains three tables (Table 1–3) presenting the information on growth and yield parameter of rice genotypes.