Stabilization, respiratory care and survival of extremely low birth weight infants transferred on the first day of life.

Objective: To assess stabilization, respiratory care and survival of extremely low birth weight (ELBW, <1000 g at birth) infants requiring emergency transfer to tertiary NICUs on the first day of life.

Study Design: Retrospective cohort study of 55 ELBW infants transported by a dedicated neonatal transport service over a 65-month period. Ventilator data were downloaded computationally.

Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings.

Although electrophysiologists have been recording intracellular neural activity routinely ever since the ground-breaking work of Hodgkin and Huxley, and extracellular multichannel electrodes have also been used frequently and extensively, a practical experimental method to track changes in membrane potential along a complete single neuron is still lacking. Instead of obtaining multiple intracellular measurements on the same neuron, we propose an alternative method by combining single-channel somatic patch-clamp and multichannel extracellular potential recordings. In this work, we show that it is possible to reconstruct the complete spatiotemporal distribution of the membrane potential of a single neuron with the spatial resolution of an extracellular probe during action potential generation.

Acceleration during neonatal transport and its impact on mechanical ventilation.

Objective: During interhospital transfer, critically ill neonates frequently require mechanical ventilation and are exposed to physical forces related to movement of the ambulance. In an observational study, we investigated acceleration during emergency transfers and if they result from changes in ambulance speed and direction or from vibration due to road conditions. We also studied how these forces impact on performance of the fabian+nCPAP evolution neonatal ventilator and on patient-ventilator interactions.

Manifold-adaptive dimension estimation revisited.

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform.