Highly Limited Phonon Polaritons within Monolayers associated with Perovskite Oxides

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. Rapamycin The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts' experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort n = 199, validation cohort n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.This study explored the anthropometric and body composition characteristics of elite female rugby union players, comparing between and within different playing positions. Thirty elite female rugby union players (25.6 ± 4.3 y, 171.3 ± 7.7 cm, 83.5 ± 13.9 kg) from New Zealand participated in this study. Physical characteristics were assessed using anthropometric (height, body mass, skinfolds) and body composition (dual-energy X-ray absorptiometry) measures. Forwards were significantly taller (p less then 0.01; d = 1.34), heavier (p less then 0.01; d = 2.19), and possessed greater skinfolds (p less then 0.01; d = 1.02) than backs. Forwards also possessed significantly greater total (p less then 0.01; d = 1.83-2.25) and regional (p less then 0.01; d = 1.50-2.50) body composition measures compared to backs. Healthy bone mineral density values were observed in both forwards and backs, with significantly greater values observed at the arm (p less then 0.01; d = 0.92) and femoral neck (p = 0.04; d = 0.77) sites for forwards. Tight-five players were significantly heavier (p = 0.02; d = 1.41) and possessed significantly greater skinfolds (p less then 0.01; d = 0.97) than loose-forwards. Tight-five also possessed significantly greater total body composition measures (p less then 0.05; d = 0.97-1.77) and significantly greater trunk lean mass (p = 0.04; d = 1.14), trunk fat mass (p less then 0.01; d = 1.84), and arm fat mass (p = 0.02; d = 1.35) compared to loose-forwards. Specific programming and monitoring for forwards and backs, particularly within forward positional groups, appear important due to such physical characteristic differences.Toxins are the major pathogenicity factors produced by numerous bacteria involved in severe diseases in humans and animals. Certain pathogenic bacteria synthesize only one toxin which is responsible for all the symptoms and outcome of the disease. For example, botulinum toxins (BoNTs) and tetanus toxin (TeNT) are the unique causal factors of botulism and tetanus, respectively. Other bacteria attack the host organism by a set of multiple toxins which synergistically act to promote the disease. This is the case of Clostridium and Staphylococcus strains which secrete wide ranges of toxins such as pore-forming toxins, membrane phospholipid damaging toxins, and other cytotoxins and toxins interacting with the immune system involved in gangrene lesion generation.Food poisoning continue to be a threat in the food industry showing a need to improve the detection of the pathogen responsible for the hospitalization cases and death. DNA-based techniques represent a real advantage and allow the detection of several targets at the same time, reducing cost and time of analysis. The development of new methodology using SYBR Green qPCR for the detection of L. monocytogenes, Salmonella spp. and E. coli O157 simultaneously was developed and a non-competitive internal amplification control (NC-IAC) was implemented to detect reaction inhibition. The formulation and supplementation of the enrichment medium was also optimized to allow the growth of all pathogens. The limit of detection (LoD) 95% obtained was less then 1 CFU/25 g for E. coli O157, and 2 CFU/25 g for Salmonella spp. and L. monocytogenes and regarding the multiplex detection a LoD 95% of 1.7 CFU/25 g was observed. The specificity, relative sensitivity and accuracy of full methodology were 100% and the use of the NC-IAC allowed the reliability of the results without interfering with the sensitivity of the methodology.