Publications by authors named "M Ogg"

Article Synopsis
  • The experiment assessed the impact of administering bovine-appeasing substances (BAS) to feeder cattle during a 42-day preconditioning phase followed by a feedlot period, focusing on their productivity and health.
  • Ninety calves were transported and split into two treatment groups: one receiving multiple doses of BAS and the other a placebo, with various parameters like body weight and blood samples monitored throughout the program.
  • Results showed no significant differences in daily gain or feed efficiency between the two groups, but the BAS group had lower plasma haptoglobin levels, indicating better health post-transport compared to the control group.
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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.

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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.

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Accurate 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.

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This 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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