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  1. Article ; Online: The Birth of the Mammalian Sleep

    Rubén V. Rial / Francesca Canellas / Mourad Akaârir / José A. Rubiño / Pere Barceló / Aida Martín / Antoni Gamundí / M. Cristina Nicolau

    Biology, Vol 11, Iss 734, p

    2022  Volume 734

    Abstract: Mammals evolved from small-sized reptiles that developed endothermic metabolism. This allowed filling the nocturnal niche. They traded-off visual acuity for sensitivity but became defenseless against the dangerous daylight. To avoid such danger, they ... ...

    Abstract Mammals evolved from small-sized reptiles that developed endothermic metabolism. This allowed filling the nocturnal niche. They traded-off visual acuity for sensitivity but became defenseless against the dangerous daylight. To avoid such danger, they rested with closed eyes in lightproof burrows during light-time. This was the birth of the mammalian sleep, the main finding of this report. Improved audition and olfaction counterweighed the visual impairments and facilitated the cortical development. This process is called “The Nocturnal Evolutionary Bottleneck”. Pre-mammals were nocturnal until the Cretacic-Paleogene extinction of dinosaurs. Some early mammals returned to diurnal activity, and this allowed the high variability in sleeping patterns observed today. The traits of Waking Idleness are almost identical to those of behavioral sleep, including homeostatic regulation. This is another important finding of this report. In summary, behavioral sleep seems to be an upgrade of Waking Idleness Indeed, the trait that never fails to show is quiescence. We conclude that the main function of sleep consists in guaranteeing it during a part of the daily cycle.
    Keywords evolutionary bottleneck ; evolution of sleep ; sleep variability ; wakeful idling ; function of sleep ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: The conserved ASTN2/BRINP1 locus at 9q33.1–33.2 is associated with major psychiatric disorders in a large pedigree from Southern Spain

    Josep Pol-Fuster / Francesca Cañellas / Laura Ruiz-Guerra / Aina Medina-Dols / Bàrbara Bisbal-Carrió / Bernat Ortega-Vila / Jaume Llinàs / Jessica Hernandez-Rodriguez / Jerònia Lladó / Gabriel Olmos / Konstantin Strauch / Damià Heine-Suñer / Cristòfol Vives-Bauzà / Antònia Flaquer

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 16

    Abstract: Abstract We investigated the genetic causes of major mental disorders (MMDs) including schizophrenia, bipolar disorder I, major depressive disorder and attention deficit hyperactive disorder, in a large family pedigree from Alpujarras, South of Spain, a ... ...

    Abstract Abstract We investigated the genetic causes of major mental disorders (MMDs) including schizophrenia, bipolar disorder I, major depressive disorder and attention deficit hyperactive disorder, in a large family pedigree from Alpujarras, South of Spain, a region with high prevalence of psychotic disorders. We applied a systematic genomic approach based on karyotyping (n = 4), genotyping by genome-wide SNP array (n = 34) and whole-genome sequencing (n = 12). We performed genome-wide linkage analysis, family-based association analysis and polygenic risk score estimates. Significant linkage was obtained at chromosome 9 (9q33.1–33.2, LOD score = 4.11), a suggestive region that contains five candidate genes ASTN2, BRINP1, C5, TLR4 and TRIM32, previously associated with MMDs. Comprehensive analysis associated the MMD phenotype with genes of the immune system with dual brain functions. Moreover, the psychotic phenotype was enriched for genes involved in synapsis. These results should be considered once studying the genetics of psychiatric disorders in other families, especially the ones from the same region, since founder effects may be related to the high prevalence.
    Keywords Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

    Zhongxing Zhang / Geert Mayer / Yves Dauvilliers / Giuseppe Plazzi / Fabio Pizza / Rolf Fronczek / Joan Santamaria / Markku Partinen / Sebastiaan Overeem / Rosa Peraita-Adrados / Antonio Martins da Silva / Karel Sonka / Rafael del Rio-Villegas / Raphael Heinzer / Aleksandra Wierzbicka / Peter Young / Birgit Högl / Claudio L. Bassetti / Mauro Manconi /
    Eva Feketeova / Johannes Mathis / Teresa Paiva / Francesca Canellas / Michel Lecendreux / Christian R. Baumann / Lucie Barateau / Carole Pesenti / Elena Antelmi / Carles Gaig / Alex Iranzo / Laura Lillo-Triguero / Pablo Medrano-Martínez / José Haba-Rubio / Corina Gorban / Gianina Luca / Gert Jan Lammers / Ramin Khatami

    Scientific Reports, Vol 8, Iss 1, Pp 1-

    2018  Volume 11

    Abstract: Abstract Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum ...

    Abstract Abstract Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing ‘ideas’ and promising candidates for future diagnostic classifications.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2018-07-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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