BackgroundFetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions.MethodsIn this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML.ResultsBased on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectivelyConclusionsOnce the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

Background: Electronic fetal monitoring (EFM) is widely applied as a routine diagnostic tool by clinicians using fetal heart rate (FHR) signals to prevent fetal hypoxia. However, visual interpretation of the FHR usually leads to significant inter-observer and intra-observer variability, and false positives become the main cause of unnecessary cesarean sections.Goal: The main aim of this study was to ensure a novel, consistent, robust, and effective model for fetal hypoxia detection.Methods: In this work, we proposed a novel computer-aided diagnosis (CAD) system integrated with an advanced deep learning (DL) algorithm. For a 1-dimensional preprocessed FHR signal, the 2-dimensional image was transformed using recurrence plot (RP), which is considered to greatly capture the non-linear characteristics. The ultimate image dataset was enriched by changing several parameters of the RP and was then used to feed the convolutional neural network (CNN). Compared to conventional machine learning (ML) methods, a CNN can self-learn useful features from the input data and does not perform complex manual feature engineering (i.e., feature extraction and selection).Results: Finally, according to the optimization experiment, the CNN model obtained the average performance using optimal configuration across 10-fold: accuracy = 98.69%, sensitivity = 99.29%, specificity = 98.10%, and area under the curve = 98.70%.Conclusion: To the best of our knowledge, this approached achieved better classification performance in predicting fetal hypoxia using FHR signals compared to the other state-of-the-art works.Significance: In summary, the satisfied result proved the effectiveness of our proposed CAD system for assisting obstetricians making objective and accurate medical decisions based on RP and powerful CNN algorithm.

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Arsenic (As) is found to be poisonous to the commercial V 2 O 5 -WO 3 /TiO 2 catalysts for selective catalytic reduction of NOx with NH 3 . The NOx conversion of catalysts declines and N 2 O formation dominates at high temperature (above 300 °C) after As poisoning. A series of activity and characterization experiments are applied to reveal the deactivation mechanism caused by As. Results indicate that doping of As on the catalysts, which exists as H 2 AsO 4 and HAsO 4 2-, doesn't seriously change surface area of catalysts or TiO 2 phase, but greatly decreases both the Lewis and Brønsted acid sites. It is found that V-OH is destroyed and less reactive As-OH is newly formed. V-OH site is crucial to the NH 3 adsorption and its destruction by As contributes to catalyst deactivation. Besides, stronger oxidizability of catalysts that resulted from more surface-active oxygen aroused by As leads to substantial non-selective catalytic reduction reaction and NH 3 oxidation at high temperature.Both of these two aspects result in lower NOx conversion and higher N 2 O formation.However, SO 4 2can provide remarkable surface acidities and the sites that destroyed by As can thus be supplied. Benefited from these new reactive acid sites, catalysts with prominent SO 4 2loading show superior arsenic resistance even with a high As content, which indicate to be a promising anti-poisoning formula. Finally, mechanism of arsenic poisoning and resistance effect of SO 4 2is proposed based on the above analysis.

G protein-coupled receptors (GPCRs) transduce pleiotropic intracellular signals in a broad range of physiological responses and disease states. Activated GPCRs can undergo agonist-induced phosphorylation by G protein receptor kinases (GRKs) and second messenger-dependent protein kinases such as protein kinase A (PKA). Here, we characterize spatially segregated subpopulations of β2-adrenergic receptor (β2AR) undergoing selective phosphorylation by GRKs or PKA in a single cell. GRKs primarily label monomeric β2ARs that undergo endocytosis, whereas PKA modifies dimeric β2ARs that remain at the cell surface. In hippocampal neurons, PKA-phosphorylated β2ARs are enriched in dendrites, whereas GRK-phosphorylated β2ARs accumulate in soma, being excluded from dendrites in a neuron maturation-dependent manner. Moreover, we show that PKA-phosphorylated β2ARs are necessary to augment the activity of L-type calcium channel. Collectively, these findings provide evidence that functionally distinct subpopulations of this prototypical GPCR exist in a single cell.