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Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.
The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.
Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.
We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.
We fabricated highly transparent and high haze ZnO:Al film for front TCO of amorphous and microcrystalline silicon solar cells. We have sputtered ZnO:Al film of 1.3 μm on the thin seed layer of about 60nm which was previously sputtered on the glass substrate by using 4% dilution of oxygen to argon gas. The ZnO:Al film grown on the seed layer had much higher crystalline phase than one without any seed layer. Our bi-layer ZnO:Al film showed low resistivity of 2.66×10-4 Ω•cm and sheet resistance of 2.08 Ω/⇐ while conventional ZnO:Al film showed resistivity of 3.24×10-4 Ω•cm and sheet resistance of 2.46 Ω/⇐. After surface texturing by 0.5% HCl wet-chemical etching, the transmittance of ZnO:Al film was increased from 83.7% to 88.1% at wavelength of 550nm through the seed layer. Also the transmittance at 800nm was increased from 82.3% to 88.9%. Especially, haze values of the ZnO:Al film were drastically increased from 58.7% to 90.6% at wavelength of 550nm by employing the seed layer. Also haze values at 800nm were increased from 22.1% to 68.1%. It is expected that the seed layer method to improve the quality of ZnO:Al film will contribute to an increase of solar cell efficiency due to the high capability of light trapping and low electrical resistivity.
The ultrafine titanium carbonitride particles (TiC0.5N0.5) with 100 nm in mean size was successfully synthesized by nitridation treatment at ordinary temperatures, 1373∼1473 K of the nanostructured half-stoichiometric titanium carbide (TiC0.5) particles, which were produced by the magnesium reduction of gaseous TiCl4+1/4C2Cl4. In addition, the nitrogen stability for the produced titanium carbonitride particles at various temperatures and vacuum conditions was investigated experimentally and compared with values calculated by an ideal solution model.
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