Hydromorphone precipitating serotonin syndrome

Opioid medications are an underappreciated cause of serotonin syndrome. Fentanyl, meperidine, and methadone are more commonly associated with this potentially life-threatening side effect. Here, we present the case of a 60-year-old man taking duloxetine, oxycodone as needed, and long-acting hydromorphone for chronic pain, who developed serotonin syndrome two days after his hydromorphone dose was increased. Due to severe agitation he required intubation and his course was notable for marked adrenergic instability. Eventually, he improved after treatment with benzodiazepines and cyproheptadine. This case highlights a rare synergistic effect from the combination of hydromorphone, duloxetine, and oxycodone resulting in serotonin syndrome.Includes bibliographical reference

Wanderlust or Wanderlost: Gender, Mobility, and Sympathy in Late-Eighteenth-Century Literature

Ingrid Horrocks Women Wanderers and the Writing of Mobility, 1784-1814 lässt die traditionellen Figurationen des männlichen Reisenden und Entdeckers hinter sich und analysiert sowohl die thematischen als auch formalen Repräsentationen der ‚widerwilligen Wanderin‘ in Werken weiblicher Schriftstellerinnen. Sie bettet dazu die Arbeiten von Charlotte Smith, Ann Radcliffe, Mary Wollstonecraft und Frances Burney in den größeren Kontext der Mobility und Sympathy Studies und betont zwei wichtige gegenderte Privilegien, die der Mehrheit der Frauen nicht zur Verfügung standen: das Reisen als eine befreiende Suche nach individueller Identität, und Sympathie als ein ethisches Produkt von losgelöster Beobachtung. Indem Horrocks die fehlende Sympathie in den schmerzvollen, endlosen Bewegungen von Frauen minutiös aufzeigt, gewinnt sie nicht nur Erkenntnisse über den sozialen und psychologischen Status der Frau in Großbritannien im späten achtzehnten Jahrhundert, sondern auch über die Rolle des Reisens in der britischen Literatur allgemein.Departing from traditional figurations of the male traveler-explorer, Ingrid Horrocks’s Women Wanderers and the Writing of Mobility, 1784-1814 analyzes women writers’ thematic as well as formal representations of the ‘reluctant woman wanderer’ figure. Situating the writings of Charlotte Smith, Ann Radcliffe, Mary Wollstonecraft, and Frances Burney in the larger context of mobility and sympathy studies, Horrocks emphasizes two important gendered privileges unavailable to the majority of women: traveling as a liberating quest for individual identity and sympathy as an ethical product of detached observation. As Horrocks meticulously illustrates the absence of sympathy or freedom in a woman’s painfully endless movement, she sheds light on not only women’s social and psychological status in late-eighteenth-century Britain but also the role of traveling in British literature at large

Establishment of the prediction equations of 1RM skeletal muscle strength in 60- to 75-year-old Chinese men and women

The purpose of this study was to establish the one-repetition maximum (1RM) prediction equations of biceps curl, bench press, and squat from the submaximal skeletal muscle strength of 4-10RM or 11-15RM in older adults. The first group of 109 participants aged 60-75 years was recruited to measure their 1RM, 4-10RM, and 11-15RM of the three exercises. The 1RM prediction equations were developed by multiple regression analyses. A second group of participants with the similar physical characteristics to the first group was used to evaluate the equations. The actual measured 1RM of the second group correlated significantly to the predicted 1RM obtained from the equations (r values were from 0.633 to 0.985), and standard error of estimate ranged from 1.08 to 5.88. Therefore, the equations can be utilized to predict 1RM from submaximal skeletal muscle strength accurately for older adults

Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making

Pre-trained language models (PLMs) have been widely used to underpin various downstream tasks. However, the adversarial attack task has found that PLMs are vulnerable to small perturbations. Mainstream methods adopt a detached two-stage framework to attack without considering the subsequent influence of substitution at each step. In this paper, we formally model the adversarial attack task on PLMs as a sequential decision-making problem, where the whole attack process is sequential with two decision-making problems, i.e., word finder and word substitution. Considering the attack process can only receive the final state without any direct intermediate signals, we propose to use reinforcement learning to find an appropriate sequential attack path to generate adversaries, named SDM-Attack. Extensive experimental results show that SDM-Attack achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT. Furthermore, our analyses demonstrate the generalization and transferability of SDM-Attack. The code is available at https://github.com/fduxuan/SDM-Attack

Trajectory tracking control based on adaptive neural dynamics for four-wheel drive omnidirectional mobile robots

There is usually the speed jump problem existing in conventional back-stepping tracking control for four-wheel drive omni-directional mobile robots, a trajectory tracking controller based on adaptive neural dynamics model is proposed. Because of the smoothness and boundedness of the output from the neural dynamics model, it produces a gradually varying tracking speed instead of the jumping speed, and the parameters are designed to avoid the control values exceeding their limits. And then, a parameter adaptive controller is presented to improve control performance. Simulation results of different paths and comparison with the conventional back-stepping technique show that the approach is effective, and the system has a good performance with smooth output

Trajectory tracking control based on adaptive neural dynamics for four-wheel drive omnidirectional mobile robots

There is usually the speed jump problem existing in conventional back-stepping tracking control for four-wheel drive omni-directional mobile robots, a trajectory tracking controller based on adaptive neural dynamics model is proposed. Because of the smoothness and boundedness of the output from the neural dynamics model, it produces a gradually varying tracking speed instead of the jumping speed, and the parameters are designed to avoid the control values exceeding their limits. And then, a parameter adaptive controller is presented to improve control performance. Simulation results of different paths and comparison with the conventional back-stepping technique show that the approach is effective, and the system has a good performance with smooth output

Evolving Connectivity for Recurrent Spiking Neural Networks

Recurrent spiking neural networks (RSNNs) hold great potential for advancing artificial general intelligence, as they draw inspiration from the biological nervous system and show promise in modeling complex dynamics. However, the widely-used surrogate gradient-based training methods for RSNNs are inherently inaccurate and unfriendly to neuromorphic hardware. To address these limitations, we propose the evolving connectivity (EC) framework, an inference-only method for training RSNNs. The EC framework reformulates weight-tuning as a search into parameterized connection probability distributions, and employs Natural Evolution Strategies (NES) for optimizing these distributions. Our EC framework circumvents the need for gradients and features hardware-friendly characteristics, including sparse boolean connections and high scalability. We evaluate EC on a series of standard robotic locomotion tasks, where it achieves comparable performance with deep neural networks and outperforms gradient-trained RSNNs, even solving the complex 17-DoF humanoid task. Additionally, the EC framework demonstrates a two to three fold speedup in efficiency compared to directly evolving parameters. By providing a performant and hardware-friendly alternative, the EC framework lays the groundwork for further energy-efficient applications of RSNNs and advances the development of neuromorphic devices