Approximately 10\% of newborns require assistance to initiate breathing at birth, and around 5\% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in assessing fetal well-being during prenatal care, enabling the detection of abnormal patterns and supporting timely obstetric interventions to mitigate fetal risks during labor. Applying artificial intelligence (AI) methods to analyze large datasets of continuous FHR monitoring episodes with diverse outcomes may offer novel insights into predicting the risk of needing breathing assistance or interventions. Recent advances in wearable FHR monitors have enabled continuous fetal monitoring without compromising maternal mobility. However, sensor displacement during maternal movement, as well as changes in fetal or maternal position, often lead to signal dropouts, resulting in gaps in the recorded FHR data. Such missing data limits the extraction of meaningful insights and complicates automated (AI-based) analysis. Traditional approaches to handle missing data, such as simple interpolation techniques, often fail to preserve the spectral characteristics of the signals. In this paper, we propose a masked transformer-based autoencoder approach to reconstruct missing FHR signals by capturing both spatial and frequency components of the data. The proposed method demonstrates robustness across varying durations of missing data and can be used for signal inpainting and forecasting. The proposed approach can be applied retrospectively to research datasets to support the development of AI-based risk algorithms. In the future, the proposed method could be integrated into wearable FHR monitoring devices to achieve earlier and more robust risk detection.

Drones have already shown that they can reliably deliver vital shipments of blood across Rwanda, drop off prescriptions to senior citizens in Florida, and help quarantining families stay safe with contactless deliveries. Now they're going to be buzzing through the skies of rural North Carolina thanks to a novel delivery service devised by drug-maker Merck and drone-maker Volansi. The plan is simple: use Volansi's 7-foot long "Gemini" quadcopter to ferry packages of cold chain medicines -- such as vaccines, glaucoma treatments, insulin, and asthma inhalers -- from Merck's Wilson, NC drug lab to the nine regional hospitals that make up Vidant Healthplex-Wilson. This medical network serves more than 1.4 million people across 29 counties in eastern North Carolina. "We've seen the world's supply chain strained like never before from the impact of Coronavirus," said Hannan Parvizian, CEO of Volansi, said in a press statement.

Model fitting often aims to fit a single model, assuming that the imposed form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a response. Model selection without regarding model uncertainty can fail to bring these patterns to light. We present multi-model penalized regression (MMPR) to acknowledge model uncertainty in the context of penalized regression. In the penalty form introduced here, we explore how different settings can promote either shrinkage or sparsity of coefficients in separate models. A choice of penalty form that enforces variable selection is applied to predict stacking force energy (SFE) from steel alloy composition. The aim is to identify multiple models with different subsets of covariates that explain a single type of response.

We're a few days into a new calendar year, but that doesn't mean 2017 is gone for good--the best writing will stay relevant for years to come. Last year may not have been the smoothest year in Silicon Valley (or anywhere, for that matter), and certainly 2018 has arrived with a laundry list of grievances against the tech giants whose once-fluffed, regulation-free cushions are quickly thinning beneath them. But innovation continued just the same. These are the themes that fascinated us most and, we're delighted to report, the stories you read most. To both celebrate and reflect on the year behind us, here are 2017's most-read Backchannel stories, arranged chronologically.