Background: There is little information regarding complications of arterial catheterization in modern clinical care. We aimed to determine the incidence of abnormal duplex vascular ultrasound and catheter related infections following perioperative arterial catheterization.
Methods: Patients requiring arterial catheterization for elective surgery were included and insertion details collected prospectively.
The American Academy of Sleep Medicine (AASM) recognizes five sleep/wake states (Wake, N1, N2, N3, REM), yet this classification schema provides only a high-level summary of sleep and likely overlooks important neurological or health information. New, data-driven approaches are needed to more deeply probe the information content of sleep signals. Here we present a self-supervised approach that learns the structure embedded in large quantities of neurophysiological sleep data.
View Article and Find Full Text PDFAccurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are flexible neural networks that not only excel at classification tasks, but also can enable data-driven discovery through un- or self-supervised learning, which requires no human annotations to the input data.
View Article and Find Full Text PDFThis experiment was designed to determine the role of preovulatory estradiol in pregnancy retention after embryo transfer (ET). Cows were synchronized with the 7-d CO-Synch + CIDR® protocol. On d0 (d-2 =CIDR® removal), cows were grouped by estrual status (estrual [Positive Control] and nonestrual), and nonestrual cows were administered Gonadotropin Releasing Hormone (GnRH) and randomly assigned to either no treatment (Negative Control) or Estradiol (0.
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