Increase in cortisol awakening response after two weeks of self-instruction for good sleep

There is growing evidence suggesting that the magnitude of cortisol awaking response (CAR), which is characterized by a profound increase of salivary cortisol secretion after awakening, plausibly reflects the level of a chronic stress, social stress, anxiety, etc. In this study the alternation of CAR at the start and at the end of two weeks session of self-instruction for good sleep was investigated; by which we anticipated that the self-instruction for good sleep would bring-forth a positive affection for the participants, and would result in decline of cortisol awakening response (CAR). Nevertheless, as a result unexpectedly, subjects did not change their sleep and dietary habits along with the instruction, moreover the increased CAR was observed. This result implies that the suggestion of an impractical instruction would solely be taken as a stressful task for participants, even though they know that it is effective to improve their sleep. On the contrary, if one develops an instruction with practicable indication for daily life, it is highly possible to observe a positive effect of the instruction on CAR

Integrating remote sensing and GIS for prediction of rice protein contents

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI