ompleters and the control group. The trial was terminated early for reasons of futility based on the results of an interim analysis, which we performed because of inclusion problems. CONCLUSIONS Our study did show a significant reduction in affective symptoms in both groups, but the differences in reduction of affective symptoms between the intervention and control groups were not significant. There were also no differences in perinatal child outcomes. Future research should examine for which women these interventions might be effective or if changes in the internet intervention might make the intervention more effective. TRIAL REGISTRATION Netherlands Trial Register NL4162; https//tinyurl.com/sdckjek. ©Hanna M Heller, Adriaan W Hoogendoorn, Adriaan Honig, Birit FP Broekman, Annemieke van Straten. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 23.03.2020.BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy. ©Cheng-Sheng Yu, Yu-Jiun Lin, Chang-Hsien Lin, Sen-Te Wang, Shiyng-Yu Lin, Sanders H Lin, Jenny L Wu, Shy-Shin Chang. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 23.03.2020.BACKGROUND Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). OBJECTIVE This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient's mortality using their longitudinal EHR data. METHODS We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinicAlok Kapoor, Edgard Granillo, Hong Yu. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 23.03.2020.Inflammatory osteolysis is governed by exacerbated osteoclastogenesis. Ample evidence points to central role of NF-kB in such pathologic responses, yet the precise mechanisms underpinning specificity of these responses remain unclear. We propose that motifs of the scaffold protein IKKg/NEMO partly facilitate such functions. As proof-of-principle, we used site-specific mutagenesis to examine the role of NEMO in mediating RANKL-induced signaling in mouse bone marrow macrophages, known as osteoclast precursors. We identified lysine (K)270 as a target regulating RANKL signaling as K270A substitution results in exuberant osteoclastogenesis in vitro and murine inflammatory osteolysis in vivo. Mechanistically, we discovered that K270A mutation disrupts autophagy, stabilizes NEMO, and elevates inflammatory burden. Specifically, K270A directly or indirectly hinders binding of NEMO to ISG15, a ubiquitin-like protein, which we show targets the modified proteins to autophagy-mediated lysosomal degradation. Taken together, our findings suggest that NEMO serves as a toolkit to fine-tune specific signals in physiologic and pathologic conditions. © 2020, Adapala et al.Across species, sleep in young animals is critical for normal brain maturation. The molecular determinants of early life sleep remain unknown. Through an RNAi-based screen, we identified a gene, pdm3, required for sleep maturation in Drosophila. Pdm3, a transcription factor, coordinates an early developmental program that prepares the brain to later execute high levels of juvenile adult sleep. PDM3 controls the wiring of wake-promoting dopaminergic (DA) neurites to a sleep-promoting region, and loss of PDM3 prematurely increases DA inhibition of the sleep center, abolishing the juvenile sleep state.  https://www.selleckchem.com/products/l-alpha-phosphatidylcholine.html  RNA-Seq/ChIP-Seq and a subsequent modifier screen reveal that pdm3 represses expression of the synaptogenesis gene Msp300 to establish the appropriate window for DA innervation. These studies define the molecular cues governing sleep behavioral and circuit development, and suggest sleep disorders may be of neurodevelopmental origin. © 2020, Chakravarti Dilley et al.