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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. 

Herein, we demonstrate that homopolymerization and statistical copolymerization of 2-ethylhexyl thiophene-3-carboxylate and 2-ethylhexyl selenophene-3-carboxylate monomers is possible via Suzuki–Miyaura cross-coupling. A commercially available palladium catalyst ([1,3-bis(2,6-di-3-pentylphenyl)imidazol-2-ylidene](3-chloropyridyl)dichloropalladium( ii ) or PEPPSI-IPent) was employed to prepare regioregular conjugated polymers with high molecular weights (∼20–30 kg mol −1 ), and relatively narrow molecular weight distributions. The optical bandgap in the copolymer series could be reduced by increasing the concentration of selenophene-3-carboxylate in the material. Configurational triads were observed in the 1 H NMR spectra of the statistical copolymers, which were assigned using a combination of 2D NMR techniques. The use of a 1 H– 77 Se HSQC spectrum to further examine sequence distribution in the statistical copolymers revealed how 77 Se NMR can be used as a tool to examine the microstructure of Se-containing conjugated polymers. 

Python's ease of use and rich collection of numeric libraries make it an excellent choice for rapidly developing scientific applications. However, composing these libraries to take advantage of complex heterogeneous nodes is still difficult. To simplify writing multi-device code, we created Parla, a heterogeneous task-based programming framework that fully supports Python's scientific programming stack. Parla's API is based on Python decorators and allows users to wrap code in Parla tasks for parallel execution. Parla arrays enable automatic movement of data between devices. The Parla runtime handles resource-aware mapping, scheduling, and execution of tasks. Compared to other Python tasking systems, Parla is unique in its parallelization of tasks within a single process, its GPU context and resource-aware runtime, and its design around gradual adoption to provide easy migration of and integration into existing Python applications. We show that Parla can achieve performance competitive with hand-optimized code while improving ease of development. 

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. 

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. 

Nuclear Overhauser Effect (NOE) methods in NMR are an important tool for 3D structural analysis of small molecules. Quantitative NOE methods conventionally rely on reference distances, known distances that have to be spectrally separated and are not always available. Here we present a new method for evaluation and 3D structure selection that does not require a reference distance, instead utilizing structures optimized by molecular mechanics, enabling NOE evaluation even on molecules without suitable reference groups.