Protective Mechanisms of Siloxane-Modified Epoxy Novolac Coatings at High-Pressure, High-Temperature Conditions.

In the context of high-pressure, high-temperature (HPHT) conditions resembling those in the oil and gas industry, the performance of epoxy-siloxane hybrid coatings is investigated. Neat amine-cured epoxy novolac (EN) coatings exhibit drawbacks under these conditions, including softening upon exposure to hydrocarbons, leading to underfilm corrosion triggered by CO gas and seawater ion diffusion. To address these issues, two hybrid coatings, long-chain epoxy-terminated polydimethylsiloxane-modified EN (EN-EPDMS) and short-chain 3-glycidyloxypropyltrimethoxysilane-modified EN (EN-GPTMS), are assessed in HPHT environments.

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST.

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.

Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation studies exist because raw data from CSTs are rarely made accessible for external use. We present a deep neural network (DNN) with a strong temporal core, inspired by U-Net, that can process multivariate time series inputs with different dimensionality to predict sleep stages (wake, light-, deep-, and REM sleep) using ACC and PPG signals from nocturnal recordings.

The sonar beam of implies ecological adaptation under phylogenetic constraint.

All animals are adapted to their ecology within the bounds of their evolutionary heritage. Echolocating bats clearly show such adaptations and boundaries through their biosonar call design. Adaptations include not only the overall time-frequency structure, but also the shape of the emitted echolocation beam.

Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts.

Study Objectives: Up to 5% of adults in Western countries have undiagnosed sleep-disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies, however, have limited generalizability as they have been conducted using the apnea-ECG database, a small sample database that lacks complex SDB cases.

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers.

Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

Study Objectives: The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals.

Dynamics of the echolocation beam during prey pursuit in aerial hawking bats.

In the evolutionary arms race between prey and predator, measures and countermeasures continuously evolve to increase survival on both sides. Bats and moths are prime examples. When exposed to intense ultrasound, eared moths perform dramatic escape behaviors.

Effect of hyperosmotic conditions on the expression of the betaine-GABA-transporter (BGT-1) in cultured mouse astrocytes.

The adaptation of cells to hyperosmotic conditions involves accumulation of organic osmolytes to achieve osmotic equilibrium and maintenance of cell volume. The Na+ and Cl(-)-coupled betaine/GABA transporter, designated BGT-1, is responsible for the cellular accumulation of betaine and has been proposed to play a role in osmoregulation in the brain. BGT-1 is also called GAT2 (GABA transporter 2) when referring to the mouse transporter homologue.