Low Temperature Precursor Route for Highly Efficient Spherically Shaped LED-Phosphors M2Si5N8:Eu2+ (M = Eu, Sr, Ba)

The highly efficient nitridosilicate phosphors M2Si5N8 (M = Sr, Ba, Eu) for phosphor-converted pc-LEDs were synthesized at low temperatures using a novel precursor route involving metal amides M(NH2)2. These precursors have been synthesized by dissolution of the respective metals in supercritical ammonia at 150°C and 300 bar. The thermal behavior and decomposition process of the amides were investigated with temperature programmed powder X-ray diffractometry and thermoanalytical measurements (DTA/TG). These investigations rendered the amides as suitable intermediates for reaction with silicon diimide (Si(NH)2). Thus, the desired nitridosilicate phosphors were obtained at relatively low temperatures around 1150−1400°C which is approximately 300°C lower compared to common synthetic approaches starting from metals or oxides. The influence of the thermal treatment on the phosphor morphology has been studied extensively. The accessibility of spherical phosphor particles represents another striking feature of this route since it improves light extraction from the crystallites due to decreasing light guiding and decreasing re-absorption inside the phosphor particle. The synthesized luminescent materials M2Si5N8:Eu2+ (M = Sr, Ba) exhibit quantum efficiencies and emission band widths (FWHM 70−90 nm) comparable to standard phosphor powders. Employment of Eu(NH2)2 as dopant reagent for synthesis of Ba2Si5N8:Eu2+ proved favorable for the formation of spherical crystallites compared to doping with Eu metal, halides, or oxide

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Influence of lactococcal surface properties on cell retention and distribution in cheese curd

During cheese manufacturing, on average 90% of the starter culture cells are believed to be entrapped in the curd, with the remainder lost in whey. This paper shows that plasmid-cured dairy strains of Lactococcus lactis show cell retention in the curd of 30-72%, whereas over-expression of pili on the lactococcal cell surface can increase cell retention to 99%. Exopolysaccharide production and cell clumping and chaining do not influence cell retention in cheese curd. L. lactis surface alteration also strongly affected the distribution of cells in the cheese matrix: clumping and over-expression of pili led to formation of large cell aggregates embedded in the protein matrix whereas exopolysaccharide expression resulted in cells being surrounding by small serum regions in the protein matrix of the cheese. These results suggest that surface properties of dairy starter cultures strongly determine retention and distribution of the bacteria in cheese curd. (C) 2018 The Authors. Published by Elsevier Ltd

A novel quantitative arousal-associated EEG-metric to predict severity of respiratory distress in obstructive sleep apnea patients

Respiratory arousals (RA) on polysomnography (PSG) are an important predictor of obstructive sleep apnea (OSA) disease severity. Additionally, recent reports suggest that more global indices of desaturation such as the hypoxic burden, namely the area under the curve (AUC) of the oxygen saturation (SaO2) PSG trace may better depict the desaturation burden in OSA. Here we investigated possible associations between a new metric, namely the AUC of the respiratory arousal electroencephalographic (EEG) recording, and already established parameters as the apnea/hypopnea index (AHI), arousal index and hypoxic burden in patients with OSA. In this data-driven study, polysomnographic data from 102 patients with OSAS were assessed (32 female; 70 male; mean value of age: 52 years; mean value of Body-Mass-Index-BMI: 31 kg/m2). The marked arousals from the pooled EEG signal (C3 and C4) were smoothed and the AUC was estimated. We used a support vector regressor (SVR) analysis to predict AHI, arousal index and hypoxic burden as captured by the PSG. The SVR with the arousal-AUC metric could quite reliably predict the AHI with a high correlation coefficient (0,58 in the training set, 0,65 in the testing set and 0,64 overall), as well as the hypoxic burden (0,62 in the training set, 0,58 in the testing set and 0,59 overall) and the arousal index (0,58 in the training set, 0,67 in the testing set and 0,66 overall). This novel arousal-AUC metric may predict AHI, hypoxic burden and arousal index with a quite high correlation coefficient and therefore could be used as an additional quantitative surrogate marker in the description of obstructive sleep apnea disease severity

Distinct EEG‐EMG‐coherence patterns associated with sleep‐disordered breathing severity grade [Abstract]

Objectives/Instruction: We investigated whether using EEG‐EMG‐coherence (EEC) as a feature fed to a support vector machine (SVM) algorithm may allow staging of disease severity among sleep‐disordered‐breathing (SDB) patients. Methods: EEG‐EMG‐coherence data resulted by applying a multitaper processing for estimating the power spectrums separately and calculating the coherence on raw C3‐/C4‐EEG‐ and EMG‐ chin data of polysomnographic (PSG) recordings of 102 SDB patients (33 female; age: 53, ± 12,4 yrs) acquired on the second of two consecutive PSG nights in each patient. Four epochs (30 s each, classified manually by AASM 2012‐ criteria) of each sleep stage (N1, N2, N3, REM) were marked (in total 1632 epochs/night) and were included in the analysis. After multitaper processing, EEC values were fed to a SVM algorithm to classify SDB disease severity based on respiratory disturbance index (RDI). Twenty patients had a mild (RDI ≥ 10/h and < 15/h), 30 patients had a moderate (RDI ≥ 15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥ 30/h). Twenty five patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator characteristic (ROC) curve. Results: EEG‐EMG coherence values could distinguish between SDB‐patients without OSA and OSA patients of the above three severity groups using an SVM algorithm. Using PSG data of the second night, in mild OSA the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions: Grading disease severity in SDB patients can be performed using PSG‐based multitaper‐processed EEC values processedwith a SVM algorithm. Disclosure: Nothing to disclose

The Randomized Shortened Dental Arch Study: Tooth Loss

The evidence concerning the management of shortened dental arch (SDA) cases is sparse. This multi-center study was aimed at generating data on outcomes and survival rates for two common treatments, removable dental prostheses (RDP) for molar replacement or no replacement (SDA). The hypothesis was that the treatments lead to different incidences of tooth loss. We included 215 patients with complete molar loss in one jaw. Molars were either replaced by RDP or not replaced, according to the SDA concept. First tooth loss after treatment was the primary outcome measure. This event occurred in 13 patients in the RDP group and nine patients in the SDA group. The respective Kaplan-Meier survival rates at 38 months were 0.83 (95% CI: 0.74-0.91) in the RDP group and 0.86 (95% CI: 0.78-0.95) in the SDA group, the difference being non-significant