Ambulatory sleep scoring using accelerometers—distinguishing between nonwear and sleep/wake states

Background. Differentiating nonwear time from sleep and wake times is essential forthe estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is re-quired. Therefore, we developed a classification algorithm to determine nonwear, sleepand wake periods from accelerometer data. Our work aimed to (I) develop a new patternrecognition algorithm for identifying nonwear periods from actigraphy data based onthe influence of respiration rate on the power spectrum of the acceleration signal andimplement it in an automatic classification algorithm for nonwear/sleep/wake states;(II) address motion artifacts that occur during nonwear periods and are known to causemisclassification of these periods; (III) adjust the algorithm depending on the sensorposition (wrist, chest); and (IV) validate the algorithm on both healthy individuals andpatients with sleep disorders. Methods. The study involved 98 participants who wore wrist and chest accelerationsensors for one day of measurements. They spent one night in the sleep laboratoryand continued to wear the sensors outside of the laboratory for the remainder of theday. The results of the classification algorithm were compared to those of the referencesource: polysomnography for wake/sleep and manual annotations for nonwear/wearclassification. Results. The median kappa values for the two locations were 0.83 (wrist) and 0.84(chest). The level of agreement did not vary significantly by sleep health (good sleepersvs. subjects with sleep disorders) (p=0.348,p=0.118) or by sex (p=0.442,p=0.456).The intraclass correlation coefficients of nonwear total time between the referenceand the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after theoutliers were removed for the wrist and chest, respectively. There was no evidence of anassociation between the mean difference (and 95% limits of agreement) and the meanof the two methods for either sensor position (wrist p=0.110, chest p=0.164), and themean differences (algorithm minus reference) were 5.11 [95% LoA−15.4–25.7] and1.32 [95% LoA−9.59–12.24] min/day, respectively, after the outliers were removed. Discussion. We studied the influence of the respiration wave on the power spectrum ofthe acceleration signal for the differentiation of nonwear periods from sleep and wakeperiods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer

Auxin-jasmonate crosstalk in Oryza sativa L. root system formation after cadmium and/or arsenic exposure

Soil pollutants may affect root growth through interactions among phytohormones like auxin and jasmonates.Rice is frequently grown in paddy fields contaminated by cadmium and arsenic, but the effects of these pollutants on jasmonates/auxin crosstalk during adventitious and lateral roots formation are widely unknown. Therefore, seedlings of Oryza sativa cv. Nihonmasari and of the jasmonate-biosynthetic mutant coleoptile photomorphogenesis2 were exposed to cadmium and/or arsenic, and/or jasmonic acid methyl ester, and then analysed through morphological, histochemical, biochemical and molecular approaches. In both genotypes, arsenic and cadmium accumulated in roots more than shoots. In the roots, arsenic levels were more than twice higher than cadmium levels, either when arsenic was applied alone, or combined with cadmium. Pollutants reduced lateral root density in the wild -type in every treatment condition, but jasmonic acid methyl ester increased it when combined with each pollutant. Interestingly, exposure to cadmium and/or arsenic did not change lateral root density in the mutant. The transcript levels of OsASA2 and OsYUCCA2, auxin biosynthetic genes, increased in the wild-type and mutant roots when pollutants and jasmonic acid methyl ester were applied alone. Auxin (indole-3-acetic acid) levels transiently increased in the roots with cadmium and/or arsenic in the wild-type more than in the mutant. Arsenic and cadmium, when applied alone, induced fluctuations in bioactive jasmonate contents in wild-type roots, but not in the mutant. Auxin distribution was evaluated in roots of OsDR5::GUS seedlings exposed or not to jasmonic acid methyl ester added or not with cadmium and/or arsenic. The DR5::GUS signal in lateral roots was reduced by arsenic, cadmium, and jasmonic acid methyl ester. Lipid peroxidation, evaluated as malondialdehyde levels, was higher in the mutant than in the wild-type, and increased particularly in As presence, in both genotypes. Altogether, the results show that an auxin/jasmonate interaction affects rice root system development in the presence of cadmium and/or arsenic, even if exogenous jasmonic acid methyl ester only slightly mitigates pollutants toxicity