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Abstract ObjectiveA mother–child dyad trajectory model of weight and body composition spanning from conception to adolescence was developed to understand how early life exposures shape childhood body composition. MethodsAfrican American (49.3%) and Dominican (50.7%) pregnant mothers (n= 337) were enrolled during pregnancy, and their children (47.5% female) were followed from ages 5 to 14. Gestational weight gain (GWG) was abstracted from medical records. Child weight, height, percentage body fat, and waist circumference were measured. GWG and child body composition trajectories were jointly modeled with a flexible latent class model with a class membership component that included prepregnancy BMI. ResultsFour prenatal and child body composition trajectory patterns were identified, and sex‐specific patterns were observed for the joint GWG–postnatal body composition trajectories with more distinct patterns among girls but not boys. Girls of mothers with high GWG across gestation had the highest BMIzscore, waist circumference, and percentage body fat trajectories from ages 5 to 14; however, boys in this high GWG group did not show similar growth patterns. ConclusionsJointly modeled prenatal weight and child body composition trajectories showed sex‐specific patterns. Growth patterns from childhood though early adolescence appeared to be more profoundly affected by higher GWG patterns in females, suggesting sex differences in developmental programming. 

Child birth via Cesarean section accounts for approximately 32% of all births each year in the United States. A variety of risk factors and complications can lead caregivers and patients to plan for a Cesarean delivery in advance before onset of labor. However, a non-trivial subset of Cesarean sections (∼25%) are unplanned and occur after an initial trial of labor is attempted. Unfortunately, patients who deliver via unplanned Cesarean sections have increased maternal morbidity and mortality rates and higher rates of neonatal intensive care admissions. In an effort to develop models aimed at improving health outcomes in labor and delivery, this work seeks to explore the use of national vital statistics data to quantify the likelihood of an unplanned Cesarean section based on 22 maternal characteristics. Machine learning techniques are used to ascertain influential features, train and evaluate models, and assess accuracy against available test data. Based on cross-validation results from a large training cohort ( n = 6,530,467 births), the gradient-boosted tree algorithm was identified as the best performer and was evaluated on a large test cohort ( n = 10,613,877 births) for two prediction scenarios. Area under the receiver operating characteristic curves of 0.77 or higher and recall scores of 0.78 or higher were obtained and the resulting models are well calibrated. Combined with feature importance analysis to explain why certain maternal characteristics lead to a specific prediction in individual patients, the developed analysis pipeline provides additional quantitative information to aid in the decision process on whether to plan for a Cesarean section in advance, a substantially safer option among women at a high risk of unplanned Cesarean delivery during labor. 

Evidence suggests that signatures of health and disease, or digital biomarkers, exist within the heterogeneous, temporally-dense data gathered from smartphone sensors and wearable devices that can be leveraged for medical applications. Modern smartphones contain a collection of energy-efficient sensors capable of capturing the device’s movement, orientation, and location as well characteristics of its external environment (e.g. ambient temperature, sound, pressure). When paired with peripheral wearable devices like smart watches, smartphones can also facilitate the collection/aggregation of important vital signs like heart rate and oxygen saturation. Here we discuss our recent experiences with deploying an open-source, cloud-native framework to monitor and collect smartphone sensor data from a cohort of pregnant women over a period of one year. We highlight two open-source integrations into the pipeline we found particularly useful: 1) a dashboard–built with Grafana and backed by Graphite–to monitor and manage production server loads and data collection metrics across the study cohort and 2) a back-end storage solution with InfluxDB, a multi-tenant time series database and data exploration ecosystem, to support biomarker discovery efforts of a multidisciplinary research team. 

Containerized applications have exploded in popularity in recent years, due to their ease of deployment, reproducible nature, and speed of startup. Accordingly, container orchestration tools such as Kubernetes have emerged as resource providers and users alike try to organize and scale their work across clusters of systems. This paper documents some real-world experiences of building, operating, and using self-hosted Kubernetes Linux clusters. It aims at comparisons between Kubernetes and single-node container solutions and traditional multi-user, batch queue Linux clusters. The authors of this paper have background experience first running traditional HPC Linux clusters and queuing systems like Slurm, and later virtual machines using technologies such as Openstack. Much of the experience and perspective below is informed by this perspective. We will also provide a use-case from a researcher who deployed on Kubernetes without being as opinionated about other potential choices. 

With the ubiquitousness of mobile smart phones, health researchers are increasingly interested in leveraging these commonplace devices as data collection instruments for near real-time data to aid in remote monitoring, and to support analysis and detection of patterns associated with a variety of health-related outcomes. As such, this work focuses on the analysis of GPS data collected through an open-source mobile platform over two months in support of a larger study being undertaken to develop a digital phenotype for pregnancy using smart phone data. An exploration of a variety of off-the-shelf clustering methods was completed to assess accuracy and runtime performance for a modest time-series of 292K non-uniform samples on the Stampede2 system at TACC. Motivated by phenotyping needs to not-only assess the physical coordinates of GPS clusters, but also the accumulated time spent at high-interest locations, two additional approaches were implemented to facilitate cluster time accumulation using a pre-processing step that was also crucial in improving clustering accuracy and scalability.