Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.</p

Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use

Laminar free-surface flow around emerging obstacles: Role of the obstacle elongation on the horseshoe vortex

International audienceAn emerging rectangular obstacle placed in a laminar boundary layer developing under a free-surface generates three vortical structures: a horseshoe vortex (HSV) in front of the obstacle, a wake downstream and two lateral recirculation zones at its sides. The present work investigates, through PIV measurements, the effect of the obstacle elongation (length over width ratio L/W) on the HSV, which is partly indirect through the modification of the two other vortical structures. Horizontal velocity fields in the near-bottom region show that an increase of the obstacle elongation leads to a higher adverse pressure gradient in front of the obstacle, and in consequence, to the longitudinal extension of the HSV. This modification of geometry, in turn, impacts the vortex dynamics of the HSV. On top of that, maps of spectra and oscillation direction obtained from velocity fields indicate that each of the three structures (HSV, wake and lateral recirculation zones) exhibits a proper oscillation frequency. As the oscillation associated to the wake is energetically dominant and is strong enough to travel upstream, it impacts the HSV dynamics for sufficiently short obstacles

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 201

Characteristics of the recirculation cell pattern in a lateral cavity

River hydrodynamicsInteraction with structure

Smoother than smooth: increasing the flow conveyance of an open-channel flow by using drag reduction methods

International audienceThe drag reduction method using polymer additives is a common strategy to minimize friction losses when carrying fluids (water, oil, or slurries) in pipes over long distances. Previous studies showed that the interactions between the polymer and turbulent structures of the flow tend to modify the streamwise velocity profile close to the walls by adding a so-called elastic sublayer between the classical viscous and log layers. The gain in linear head losses can reach up to 80% depending on the roughness of the walls and the concentration of polymers. The application of this technique to sewers and the subsequent gain in discharge capacity motivated this work to quantitatively measure the drag reduction in classical open-channel flows. Three measurement campaigns were performed in a dedicated long flume for several water discharges and several polymer concentrations: backwater curves over smooth and rough channel walls (including velocity and turbulent shear-stress profiles) and flows around emerging obstacles. The addition of polymers, even in limited concentrations, allowed a high friction decrease with the typical Darcy-Weisbach coefficient reduced by factors of 2 and 1.5, respectively, in smooth and rough walls configurations without obstacles, but without strong modifications of the nondimensional velocity profiles. In contrast, when adding emerging obstacles, the flow was unaffected by the inclusion of polymers, in agreement with the prediction of the literature. The drag reduction method by addition of small concentrations of polymers thus appears to be a promising technique to increase flow conveyance in open-channel flows

Depression: Relationships to Sleep Paralysis and Other Sleep Disturbances in a Community Sample

Sleep disturbances are important correlates of depression, with epidemiologic research heretofore focused on insomnia and sleepiness. This epidemiologic study’s aim was to investigate, in a community sample, depression’s relationships to other sleep disturbances: sleep paralysis (SP), hypnagogic/hypnopompic hallucinations (HH), cataplexy – considered rapid eye movement-related disturbances – and automatic behavior (AB). Although typical of narcolepsy, these disturbances are prevalent, albeit under-studied, in the population. Cross-sectional analyses (1998–2002), based on Wisconsin Sleep Cohort Study population-based data from 866 participants (mean age 54, 53% male), examined: depression (Zung Self-Rating Depression Scale), trait anxiety (Spielberger State-Trait Anxiety Inventory, STAI-T ≥ 75th percentile), and self-reported sleep disturbances. Descriptive sleep data were obtained by overnight polysomnography. Adjusted logistic regression models estimated depression’s associations with each (\u3efew times ever) outcome – SP, HH, AB, and cataplexy. Depression’s associations with self-reported SP and cataplexy were not explained by anxiety. After anxiety adjustment, severe depression (Zung ≥55), vis-à-vis Zung \u3c50, increased SP odds ∼500% ( P = 0.0008). Depression (Zung ≥50), after stratification by anxiety given an interaction ( P = 0.02), increased self-reported cataplexy odds in non-anxious (OR 8.9, P = 0.0008) but not anxious (OR 1.1, P = 0.82) participants. Insomnia and sleepiness seemed only partial mediators or confounders for depression’s associations with self-reported cataplexy and SP. Anxiety (OR 1.9, P = 0.04) partially explained depression’s (Zung ≥55) association with HH (OR 2.2, P = 0.08). Anxiety (OR 1.6, P = 0.02) was also more related than depression to AB. Recognizing depression’s relationships to oft-neglected sleep disturbances, most notably SP, might assist in better characterizing depression and the full range of its associated sleep problems in the population. Longitudinal studies are warranted to elucidate mediators and causality

Genetics of Sleep and Sleep Disorders

Sleep remains one of the least understood phenomena in biology—even its role in synaptic plasticity remains debatable. Since sleep was recognized to be regulated genetically, intense research has launched on two fronts: the development of model organisms for deciphering the molecular mechanisms of sleep and attempts to identify genetic underpinnings of human sleep disorders. In this Review, we describe how unbiased, high-throughput screens in model organisms are uncovering sleep regulatory mechanisms and how pathways, such as the circadian clock network and specific neurotransmitter signals, have conserved effects on sleep from Drosophila to humans. At the same time, genome-wide association studies (GWAS) have uncovered ∼14 loci increasing susceptibility to sleep disorders, such as narcolepsy and restless leg syndrome. To conclude, we discuss how these different strategies will be critical to unambiguously defining the function of sleep