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In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development. Measurement(s) vital signs of patients during surgery • perioperative patient information Technology Type(s) Vital Signs Measurement • Electronic Medical Record Factor Type(s) vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location South Korea [ABSTRACT FROM AUTHOR]

Few materials have been identified as high-performance transparent conductors in the visible regime (400–700 nm). Even fewer conductors are known to be transparent in ultraviolet (UV) spectrum, especially at wavelengths below 320 nm. Doped wide-bandgap semiconductors employed currently as UV transparent conductors have insufficient electrical conductivities, posing a significant challenge for achieving low resistance electrodes. Here, we propose SrNbO3 as an alternative transparent conductor material with excellent performance not only in the visible, but also in the UV spectrum. The high transparency to UV light originates from energetic isolation of the conduction band, which shifts the absorption edge into the UV regime. The standard figure of merit measured for SrNbO3 in the UV spectral range of 260–320 nm is on par with indium tin oxide in the visible, making SrNbO3 an ideal electrode material in high-performance UV light emitting diodes relevant in sanitation application, food packaging, UV photochemotherapy, and biomolecule sensing. Optically transparent electrodes with high electrical conductance are essential for the implementation of optoelectronics, but current technology performs poorly in the ultraviolet regime. Here, SrNbO3 is proposed as an alternative material due to its high figure of merit in the ultraviolet range. [ABSTRACT FROM AUTHOR]

Orthorhombic RMnO3 (R = rare-earth cation) compounds are type-II multiferroics induced by inversion-symmetry-breaking of spin order. They hold promise for magneto-electric devices. However, no spontaneous room-temperature ferroic property has been observed to date in orthorhombic RMnO3. Here, using 3D straining in nanocomposite films of (SmMnO3)0.5((Bi,Sm)2O3)0.5, we demonstrate room temperature ferroelectricity and ferromagnetism with TC,FM ~ 90 K, matching exactly with theoretical predictions for the induced strain levels. Large in-plane compressive and out-of-plane tensile strains (−3.6% and +4.9%, respectively) were induced by the stiff (Bi,Sm)2O3 nanopillars embedded. The room temperature electric polarization is comparable to other spin-driven ferroelectric RMnO3 films. Also, while bulk SmMnO3 is antiferromagnetic, ferromagnetism was induced in the composite films. The Mn-O bond angles and lengths determined from density functional theory explain the origin of the ferroelectricity, i.e. modification of the exchange coupling. Our structural tuning method gives a route to designing multiferroics. Multiferroic materials exhibiting ferromagnetism (FM) and ferroeletricity (FE) at room temperature (RT) are promising for applications. Here, the authors demonstrate by inducing strain in SmMnO3 via introducing vertically aligned nanocomposites that exchange coupling can be modified tuning the system from anti-FM to FM and simultaneously inducing FE at RT. [ABSTRACT FROM AUTHOR]

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice. [ABSTRACT FROM AUTHOR]