Difference in the impact of coinfections and secondary infections on antibiotic use in patients hospitalized with COVID-19 between the Omicron-dominant period and the pre-Omicron period.

INTRODUCTION: This study evaluated the effect of coinfections and/or secondary infections on antibiotic use in patients hospitalized with coronavirus disease 2019 (COVID-19). METHOD: Days of therapy per 100 bed days (DOT) in a COVID-19 ward were compared between 2022 (Omicron period) and 2021 (pre-Omicron period). Antibiotics were categorized as antibiotics predominantly used for community-acquired infections (CAIs) and antibiotics predominantly used for health care-associated infections (HAIs). Bacterial and/or fungal infections which were proved or assumed on admission were defined as coinfections. Secondary infections were defined as infections that occurred following COVID-19. RESULTS: Patients with COVID-19 during the Omicron period were older and had more comorbidities. Coinfections were more common in the Omicron period than in the pre-Omicron period (44.4% [100/225] versus 0.8% [2/257], respectively, p < 0.001), and the mean DOT of antibiotics for CAIs was significantly increased in the Omicron period (from 3.60 to 17.84, p < 0.001). Secondary infection rate tended to be higher in the Omicron period (p = 0.097). Mean DOT of antibiotics for HAIs were appeared to be lower in the COVID-19 ward than in the general ward (pre-Omicron, 3.33 versus 6.37, respectively; Omicron, 3.84 versus 5.22, respectively). No multidrug-resistant gram-negative organisms were isolated in the COVID-19 ward. CONCLUSION: Antibiotic use for CAIs was limited in the pre-Omicron period but increased in the Omicron period because of a high coinfection rate on admission. With the antimicrobial stewardship, excessive use of antibiotics for HAIs was avoided in the COVID-19 ward during both periods.

Collaborative hunting in artificial agents with deep reinforcement learning.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

From wolves to ants, many animals are known to be able to hunt as a team. This strategy may yield several advantages: going after bigger preys together, for example, can often result in individuals spending less energy and accessing larger food portions than when hunting alone. However, it remains unclear whether this behavior relies on complex cognitive processes, such as the ability for an animal to represent and anticipate the actions of its teammates. It is often thought that 'collaborative hunting' may require such skills, as this form of group hunting involves animals taking on distinct, tightly coordinated roles ­ as opposed to simply engaging in the same actions simultaneously. To better understand whether high-level cognitive skills are required for collaborative hunting, Tsutsui et al. used a type of artificial intelligence known as deep reinforcement learning. This allowed them to develop a computational model in which a small number of 'agents' had the opportunity to 'learn' whether and how to work together to catch a 'prey' under various conditions. To do so, the agents were only equipped with the ability to link distinct stimuli together, such as an event and a reward; this is similar to associative learning, a cognitive process which is widespread amongst animal species. The model showed that the challenge of capturing the prey when hunting alone, and the reward of sharing food after a successful hunt drove the agents to learn how to work together, with previous experiences shaping decisions made during subsequent hunts. Importantly, the predators started to exhibit the ability to take on distinct, complementary roles reminiscent of those observed during collaborative hunting, such as one agent chasing the prey while another ambushes it. Overall, the work by Tsutsui et al. challenges the traditional view that only organisms equipped with high-level cognitive processes can show refined collaborative approaches to hunting, opening the possibility that these behaviors may be more widespread than originally thought ­ including between animals of different species.

Neural-circuit basis of song preference learning in fruit flies.

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the brain, with these neurons playing a key role in mating behavior. The neural circuit basis of this GABAergic input, however, is not known. Here, we find that GABAergic neurons expressing the sex-determination gene doublesex are necessary for song preference learning. In the brain, only four doublesex-expressing GABAergic neurons exist per hemibrain, identified as pCd-2 neurons. pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between them. Moreover, GABAergic and dopaminergic inputs to doublesex-expressing GABAergic neurons are necessary for song preference learning. Together, this study provides a neural circuit model that underlies experience-dependent auditory plasticity at a single-cell resolution.