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Children are at an increased risk of medication errors (MEs) during perioperative care compared to adult patients. This study aimed to critically look at medication errors and determine the frequency of adverse drug events and corrective measures taken for medication errors reported over 20 years in pediatric anestheti...	Medication errors and adverse drug events in peri-operative pediatric anesthetic care over twenty years: a retrospective observational study.
Primates, including humans, use stimulus-reward associations to guide foraging. We previously showed that both the rhinal cortex (Rh) and rostromedial caudate (rmCD) of rhesus monkeys play causal roles in assigning value to visual stimuli. Layer 5 neurons in Rh project to rmCD. Here, we reversibly interrupted this Laye...	Chemogenetic Disruption of Monkey Perirhinal Neurons Projecting to the Rostromedial Caudate Impairs Associative Learning.
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of novel therapeutic targets. This study explores the potential of antimic...	Machine learning-driven discovery of antimicrobial peptides targeting the GAPDH-TPI protein-protein interaction in Schistosoma mansoni for novel antischistosomal therapeutics.
This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftly adjust agent strategies across a range of games, while STDP fine-tun...	Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance.
Metacognition- namely the capacity to reflect on one's own cognitive processes - provides animals with numerous evolutionary advantages. Metacognition abilities encompass enhanced decision-making in uncertain situations, more efficient resource management, error detection and correction, and improved problem-solving sk...	Uncertainty monitoring in Eurasian jays (Garrulus glandarius).
Currently, preclinical research has reported conflicting evidence as to whether chronic pain imparts resilience or vulnerability to opioid drug seeking. Here, we investigated the impact of chronic pain on the intravenous self-administration (IVSA) profile of the short-acting opioid analgesic remifentanil in a mouse mod...	Chronic pain selectively reduces the motivation to work for remifentanil but not food reward.
Understanding the world in terms of objects and the possible interactions with them is an important cognitive ability. However, current world models adopted in reinforcement learning typically lack this structure and represent the world state in a global latent vector. To address this, we propose FOCUS, a model-based a...	FOCUS: object-centric world models for robotic manipulation.
Active nematics are paradigmatic active matter systems which generate micron-scale patterns and flows. Recent advances in optical control over molecular motors now allow experimenters to control the non-equilibrium activity field in space and time and, in turn, the patterns and flows. However, engineering effective act...	Tailoring interactions between active nematic defects with reinforcement learning.
COVID-19 has added an impetus to an already growing trend around the use of Ambient Assisted Living (AAL) technologies to support frail seniors who live alone. The challenge, however, is that systematic research on the long-term usage of AAL technologies remains in its nascent stages, leaving gaps in understanding of t...	Integration of Ambient Assisted Living Technologies in Older Adults' Care: Lessons Learned from a Longitudinal Study.
Hilbert space dimension is a key resource for quantum information processing<sup>1,2</sup>. Not only is a large overall Hilbert space an essential requirement for quantum error correction, but a large local Hilbert space can also be advantageous for realizing gates and algorithms more efficiently<sup>3-7</sup>. As a re...	Quantum error correction of qudits beyond break-even.
In Wireless Sensor Networks (WSNs), achieving optimal coverage in dynamic environments remains a significant challenge. Traditional optimization techniques, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have demonstrated adaptability in node placement but struggle with real-time ...	Optimizing coverage in wireless sensor networks using deep reinforcement learning with graph neural networks.
Choice behaviour of animals is characterized by two main tendencies: taking actions that led to rewards and repeating past actions<sup>1,2</sup>. Theory suggests that these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error to reinforce value-based associations and...	Dopaminergic action prediction errors serve as a value-free teaching signal.
The Advanced Persistent Threat (APT) poses significant security challenges to the availability and reliability of government and enterprise information systems. Due to the high concealment and long duration characteristic of APT, industry and academia typically adopt active defense methods to combat APT. By deploying d...	Multiple deception resources deployment strategy based on reinforcement learning for network threat mitigation.
This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB<sub>2</sub>) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB<sub>2</sub>-CDA/Al6061 composites was systematically analyzed under dry sl...	Dry sliding tribological characteristics evaluation and prediction of TiB<sub>2</sub>-CDA/Al6061 hybrid composites exercising machine learning methods.
Some features of communicative signals may only direct the attention of the receiver to the signaller, and others may convey specific aspects of the message. Dogs rapidly engage in complex interactions with artificial agents allowing to test whether auditory cues of these agents can only serve as attention-getting cues...	Not the presence but the timing of acoustic signals influence dogs' behaviour toward an artificial agent.
Impaired regulation of food intake underlies numerous health problems, including obesity and type 2 diabetes, yet how brain systems controlling reward seeking become dysregulated to promote overeating is unknown. Glutamatergic neurons of the lateral hypothalamic area (LHA) are thought to act as a brake on feeding, whic...	Energy state guides reward seeking via an extended amygdala to lateral hypothalamus pathway.
Poor cognitive control (CC, i.e., low-level executive functions) capacity and increased reward sensitivity (RS) represent core traits and meaningful predictors in developing externalizing disorders. The inclusion of the limited-prosocial-emotions specifier (also termed callous-unemotional (CU) traits) into the DSM-5 di...	Association between callous unemotional traits cognitive control performance and reward sensitivity in youths with conduct problems - A systematic review and meta-analysis.
Ninety-seven 19- to 23-month-olds (53 boys, 44 girls; White (56%), multi-racial (25%), Asian (12%), Black (3%), other (3%)) listened to a digital book with a narration that labeled and requested the child touch an on-screen object. Conditions differed in how the book proceeded: no contingency/no reinforcement - the pag...	Reinforcement matters: Animated reinforcements disrupt young children's word learning from a digital book.
Extended goals necessitate extended commitment. We address how humans select between multiple goals in a temporally extended setting. We probe whether humans engage in prospective valuation of goals by estimating which goals are likely to yield future success and choosing those, or whether they rely on a less optimal r...	Building momentum: A computational account of persistence toward long-term goals.
In this article, an online reinforcement learning (RL) control method through value iteration (VI) is developed to solve the optimal cooperative control problem for the unknown linear discrete-time multiagent systems (MASs). On the one hand, an online learning scheme with evolving policies is proposed in order to guara...	Online Reinforcement Learning Control Designs With Acceleration Mechanism for Unknown Multiagent Systems Through Value Iteration.
With the increase in life expectancy and the rapid evolution of daily life technologies, older adults must constantly learn new skills to adapt to society. Sleep reinforces skills acquired during the day and is associated with the occurrence of specific oscillations such as spindles. However, with age, spindles deterio...	Impact of spindle-inspired transcranial alternating current stimulation during a nap on sleep-dependent motor memory consolidation in healthy older adults.
Schemas are affective-cognitive conceptual models of self, others and the world, derived from life experience. Predictive Coding theory proposes schema are created from perceptual input as follows: Based on previous similar experiences, the brain generates schema, with "predictions," expectations of future sensory expe...	Early maladaptive schemas from child maltreatment in depression and psychotherapeutic remediation: a predictive coding framework.
Over the years, many approaches have been proposed to build ancestral recombination graphs (ARGs), graphs used to represent the genetic relationship between individuals. Among these methods, many rely on the assumption that the most likely graph is among those with the fewest recombination events. In this paper, we pro...	Constructing ancestral recombination graphs through reinforcement learning.
To enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluated model performance ...	Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets.
Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such a...	Evaluating the Quality of Brain MRI Generators.
<b>Background/Objectives</b>: To investigate the impact of pachychoroid on the clinical features of neovascular age-related macular degeneration (nAMD) in Japan using the deep learning-based Hokkaido University pachychoroid index (HUPI), which has a high discriminative ability for pachychoroid. <b>Methods</b>: This ret...	Inter-Relationships Between the Deep Learning-Based Pachychoroid Index and Clinical Features Associated with Neovascular Age-Related Macular Degeneration.
There were errors in the original publication [...].	Correction: Ou et al. Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning. <i>Sensors</i> 2024, <i>24</i>, 3895.
Designing an efficient task offloading system is essential in the Industrial Internet of Things (IIoT). Owing to the limited computational capability of IIoT devices, offloading tasks to edge servers enhances computational efficiency. When an edge server is overloaded, it may experience interruptions, preventing it fro...	Interruption-Aware Computation Offloading in the Industrial Internet of Things.
Human exploration and rescue in unstructured environments including hill terrain and depression terrain are fraught with danger and difficulty, making autonomous vehicles a promising alternative in these areas. In flat terrain, traditional wheeled vehicles demonstrate excellent maneuverability; however, their passabili...	A Novel Integrated Path Planning and Mode Decision Algorithm for Wheel-Leg Vehicles in Unstructured Environment.
Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management syst...	Enhancing Airport Traffic Flow: Intelligent System Based on VLC, Rerouting Techniques, and Adaptive Reward Learning.
The Space-Air-Ground Integrated Network (SAGIN) has emerged as a core architecture for future intelligent communication due to its wide-area coverage and dynamic heterogeneous characteristics. However, its high latency, dynamic topology, and privacy-security challenges severely constrain the application of Federated Le...	Privacy-Preserving Federated Learning for Space-Air-Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework.
Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-d...	Crowd Evacuation in Stadiums Using Fire Alarm Prediction.
Autonomous navigation and target search for unmanned aerial vehicles (UAVs) have extensive application potential in search and rescue, surveillance, and environmental monitoring. Reinforcement learning (RL) has demonstrated excellent performance in real-time UAV navigation through dynamic optimization of decision-makin...	A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search.
How to further improve the fuel economy and emission performance of hybrid vehicles through scientific and reasonable energy management strategies has become an urgent issue to be addressed at present. This paper proposes an energy management model based on speed prediction using Long Short-Term Memory (LSTM) neural ne...	Long Short-Term Memory-Model Predictive Control Speed Prediction-Based Double Deep Q-Network Energy Management for Hybrid Electric Vehicle to Enhanced Fuel Economy.
Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell ...	Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning.
Agriculture needs to produce more with fewer resources to satisfy the world's demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier...	Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators.
In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupled with a forward kinematics error compensation model, is introduced t...	Research on High-Precision Motion Planning of Large Multi-Arm Rock Drilling Robot Based on Multi-Strategy Sampling Rapidly Exploring Random Tree.
The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used t...	Nondestructive Testing of Externally Bonded FRP Concrete Structures: A Comprehensive Review.
Dental education often struggles to bridge the gap between theoretical knowledge and clinical application. Traditional teaching methods may fail to meet individual learning needs, potentially impacting student performance and confidence. Artificial intelligence (AI)-driven platforms like Gemini offer personalized learn...	Empowering Future Dentists: A Comprehensive Mixed-Methods Exploration of Artificial Intelligence in Personalizing Year 3 Clinical Dental Practice Education.
Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for assembling such predictions to a full synthesis plan that, starting...	Generating diversity and securing completeness in algorithmic retrosynthesis.
Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. Thes...	Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization.
Value-driven attentional capture (VDAC) involves involuntary attention shifts towards neutral stimuli previously associated with rewards. This phenomenon is pertinent for various neuropsychiatric conditions. This study aims to evaluate the reliability of VDAC using reaction time (RT) and data-limited accuracy measures....	Single scores in the RT-based value-driven attentional capture paradigm have high reliability.
The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynam...	Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning.
The integration of large language models (LLMs) into drug design is gaining momentum; however, existing approaches often struggle to effectively incorporate three-dimensional molecular structures. Here, we present Token-Mol, a token-only 3D drug design model that encodes both 2D and 3D structural information, along wit...	Token-Mol 1.0: tokenized drug design with large language models.
Cocaine remains the most abused stimulant, causing considerable morbidity and mortality. Despite decades of research, there is no FDA-approved medication to treat cocaine use disorder (CUD). In individuals with cocaine and opioid dependence/abuse, extended-release injectable naltrexone (XR-NTX) and sublingual buprenorp...	Randomized, placebo-controlled trial of injectable extended-release naltrexone and injectable extended-release buprenorphine for cocaine use disorder (CURB-2): Study rationale and design.
The primary focus of this research is to develop an adaptive output feedback controller designed to minimize a cost-to-go function subject to constraints on input, output, and tracking error for a class of unknown non-affine discrete-time systems. The problem formulation addresses a general class of non-affine dynamics...	Model-free reinforcement learning control with zero-min barrier functions for constrained systems.
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that currently affects an estimated 6.9 million people in the United States. Despite the growing prevalence of AD, management of this common condition remains suboptimal. To address knowledge and practice gaps related to cognitive evaluation and Alzh...	Knowledge, confidence, and behavioral changes after an Alzheimer's disease continuing education program for nurse practitioners.
Previous studies on fear extinction have primarily focused on repeated exposure to fear-inducing context without negative consequences. However, it is also possible that an individual can reduce fear responses by predicting that they can transition to a safe environment through their own actions, even if a fear-inducin...	Transition ability to safe states reduces fear responses to height.
Building the skills and knowledge necessary to practice evidence-based veterinary medicine (EBVM) should occur throughout the veterinary curriculum. Operationalizing EBVM includes asking a clinical question in PICO format, searching the biomedical literature for evidence, critically appraising the evidence, and applyin...	Incorporating the Competencies of Evidence-Based Veterinary Medicine Focused on Pharmacotherapeutics Into Clinical Rotations for Small Animal Dermatology and Food Animal Medicine and Surgery at a Veterinary Medical Teaching Hospital in the US.
Dental education blends theoretical concepts with practical tasks, where preclinical simulations using manikins have long been integral. However, the limitations of manikin-based training, such as cost, material restrictions, and inter-rater reliability concerns, have led to the integration of emerging technologies lik...	Exploring the frontier of dental education: a cross-sectional study of VR simulation and manikin-based training at Ziauddin university.
Reward processing involves several prefrontal cortex areas, enabling individuals to learn from behavioral outcomes and shape decisions. However, the role of the frontopolar cortex (FPC) in these processes remains unclear due to limited single-neuron research. In this study, we recorded neural activity from the FPC of t...	Reward monitoring in the frontopolar cortex of macaques.
Beyond their incentive value, visual sexual stimuli are thought to have intrinsically rewarding properties that may contribute to the rising prevalence of problematic pornography use. However, whether excessive consumption of visual sexual stimuli fits classic models of addiction and involves reinforcement-based learni...	Trait sexual motivation shapes cue reactivity in visual, but not auditory, sexual reward learning: Psychophysiological and computational evidence.
Persistent impulsive choice, preference for a smaller-sooner over a larger-later reward, is associated with consequential life outcomes. Procedures that reduce nonhuman impulsive choice often have long training durations that reduce their translational utility. This experiment sought to alter the behavioral function of...	A Pavlovian, conditioned-reinforcement approach to reducing impulsive choice.
Bazi Bushen Capsule (BZBS), a traditional Chinese medicine formulation composed of multiple bioactive herbal components, has been validated in multicenter randomized double-blind controlled trials for its potent anti-aging properties. Previous studies from our group have demonstrated that BZBS effectively restores gut ...	Bazi Bushen capsule modulates Akkermansia muciniphila and spermidine metabolism to attenuate brain aging in SAMP8 mice.
Substance use disorders disrupt the dopaminergic system of the human brain, which plays a central role in movement and reward processing, altering perception, and cognition. The pleasurable urge to move to music, known as groove, relies on dopamine for reward, anticipation, beat perception, and motor system activity. U...	Individuals with substance use disorders experience an increased urge to move to complex music.
Legalization of recreational cannabis use is expanding across the United States, and prenatal cannabis has steadily increased. Evidence suggests that many pregnant individuals use cannabis to relieve symptoms like nausea. Research has demonstrated an association between prenatal cannabinoid exposure and infant deficits...	The protocol for a pilot feasibility trial of Improving Neurodevelopmental ouTcomes After prenatal Cannabinoid in uTero exposure (INTACT) study for a multi-center trial.
Trial-based theories of associative learning propose that learning is sensitive to the probability of reinforcement signaled by a conditioned stimulus (CS). Learning, however, is often sensitive to reinforcement rate rather than probability of reinforcement per trial, suggesting that temporal properties of cues may be ...	Probability and rate of reinforcement in negative prediction error learning.
Interprofessional education for collaborative practice (IPECP) within pre-licensure health education supports development of collaborative healthcare teams. However, challenges to enacting collaboration exist within contemporary healthcare practice. This study explores the professional socialization experiences of rece...	'It Takes a Village to Raise a Resident': Lessons Learned on Interprofessional Socialization and Collaborative Practice from Recent Medical Graduates.
Limited or delayed exposure to neurology decreases interest and reduces the likelihood of pursuing neurology as a career for medical trainees. Educational events that strengthen student-to-patient interactions may help dispel misinformation about neurologic treatments and outcomes. Innovative educational strategies suc...	Curriculum Innovation: Neuro Day: An Innovative Educational Intervention to Enhance Interest in Neurology for Medical Trainees.
Social determinants of health (SDOH) play a critical role in the onset and progression of chronic kidney disease (CKD). Despite the well-established role of SDOH, previous studies have not fully incorporated these factors in predicting CKD in Type 2 diabetes patients. To bridge this gap, this study aimed to develop and...	Explainable machine learning model incorporating social determinants of health to predict chronic kidney disease in type 2 diabetes patients.
Clinical decisions for stroke treatments, such as thrombolytic drugs for ischemic strokes or anticoagulants for hemorrhagic strokes, rely on accurate diagnosis and severity assessment. Our study uses diffusion-weighted magnetic resonance imaging and Convolutional Neural Networks (CNNs) to differentiate healthy and stro...	Multi-classification Deep Learning Approach for Diagnosing Stroke Type and Severity Using Multimodal Magnetic Resonance Images.
In recent years Covid-19 impact is causing unprecedented difficulties worldwide, affecting lifestyle choices. The post-pandemic era has made this even more critical.COVID-19 triggers widespread inflammation throughout the body, potentially causing damage to the heart and other vital organs. Mortality data from COVID-19...	Prescriptive analytics decision-making system for cardiovascular disease prediction in long COVID patients using advanced reinforcement learning algorithms.
Hypersonic vehicle interception raises stringent and challenging requirements for traditional guidance laws in terms of speed and maneuverability advantage. Deep reinforcement learning algorithms provide potential solutions for intercepting maneuvering targets of speed advantage, but they are greatly hindered by policy...	An event-triggered deep reinforcement learning guidance algorithm for intercepting maneuvering target of speed advantage.
Interprofessional collaboration (IPC) is vital for delivering safe, holistic patient care, particularly in outpatient interventional pain clinics where precision and teamwork are crucial. Despite its importance, IPC within outpatient pain medicine remains understudied, and the Readiness for Interprofessional Learning S...	Improving interprofessional collaboration in pain clinics through simulation: a longitudinal Readiness for Interprofessional Learning Scale assessment.
Pavlovian conditioning can be used to model maladaptive associations seen in anxiety/trauma and substance use disorders. One approach to attenuate conditioned responses is extinction learning (which underlies exposure therapy), wherein cues are repeatedly presented without the expected fearful or rewarding outcome. Ext...	Associations of CO<sub>2</sub> reactivity and orexin activity with extinction memory to fear and reward cues: results from a large sample of male rats across multiple studies.
The pharmacy profession has encountered significant change to its scope of practice over the last two decades. Globally, this has precipitated a need for pharmacy governing bodies to update professional development frameworks and support structures to meet the challenge of future-proofing the pharmacy workforce. It is ...	Identifying the educational needs of pharmacists engaging in professional development: A global systematic review.
Twenty-four rats were initially trained to press a target lever under a variable-interval 30-s schedule of food delivery. Then, responses on that lever were extinguished and responses on an alternative lever were reinforced under the same schedule. Finally, target responses continued to be extinguished for all rats. Fo...	Removing the opportunity to respond induces resurgence.
Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios-such as disease progression and drug pharmacokinetic processes-exhibit dynamic behaviors. Consequently, static graph methods often struggle to robustly address new environments characterized by...	Robust temporal knowledge inference via pathway snapshots with liquid neural network.
Learning accurate dynamics models is crucial for model-based reinforcement learning. Gaussian processes (GPs), as a probabilistic modeling approach, have been widely used for dynamical system modeling. However, standard GPs are designed for single-output scenarios, modeling each dimension of a dynamical system independ...	Physics-informed multi-output Gaussian process for dynamical system modeling.
Individuals with anorexia nervosa (AN) are typically anhedonic, leading to the suggestion that intrinsic disturbances of reward processing may represent a trait marker of the disorder. Previous studies have used task-based functional magnetic resonance imaging (fMRI) to investigate reward-related brain activity in AN a...	Reward contamination in restrictive anorexia nervosa: A meta-analysis of functional MRI studies.
Emerging technologies have the potential to revolutionize transportation, with Autonomous Vehicles (AVs) enhancing traffic safety, improving efficiency, and reducing emissions by optimizing driving patterns and minimizing idling time. However, despite their great potential, the actual utility and functionality of AVs h...	Generating risky and realistic scenarios for autonomous vehicle tests involving powered two-wheelers: A novel reinforcement learning framework.
Despite the relatively limited number of serotonergic neurons in humans, serotonin plays a key role in neurophysiological functions, including sleep, pain perception, learning, memory, cognition, emotion, reward, and mood regulation. Altered serotonergic neurotransmission is linked to conditions such as anxiety, depres...	5-HT6 receptors: Contemporary views on their neurobiological and pharmacological relevance in neuropsychiatric disorders.
Subcortical nuclei of the ascending arousal system (AAS) play an important role in regulating brain and cognition. However, functional MRI (fMRI) of these nuclei in humans involves unique challenges due to their size and location deep within the brain. Here, we used ultra-high-field MRI and other methodological advance...	Subcortical nuclei of the human ascending arousal system encode anticipated reward but do not predict subsequent memory.
Identifying governing equations from observational data is crucial for understanding nonlinear physical systems but remains challenging due to the risk of overfitting. Here we introduce the Bi-Level Identification of Equations (BILLIE) framework, which simultaneously discovers and validates equations using a hierarchic...	Bi-level identification of governing equations for nonlinear physical systems.
When planning an action sequence, it has been shown that humans prune decision trees to reduce computational complexity, instead of considering all possible options. However, little is understood about pruning employed in probabilistic environments, where actions result in multiple outcomes with varying probabilities, ...	Heuristic pruning of decision trees at low probabilities and probability discounting in sequential planning in young and older adults.
With the rapid advancement of technology, aerial interaction patterns have become increasingly complex, making intelligent air combat a prominent and cutting-edge research area in multi-agent systems. In this context, the dynamic and uncertain nature of large-scale air combat scenarios poses significant challenges, inc...	Autonomous air combat decision making via graph neural networks and reinforcement learning.
Methamphetamine (MA) use disorder has become a global public health problem, and the peripheral mechanisms underlying exercise as a potential treatment for MA addiction are still not fully understood. This study aims to identify a plasma metabolic biomarker in MA-administered mice under exercise interventions. The peri...	Metabolomics changes after exercise intervention reveal potential peripheral biomarkers in repeated methamphetamine exposure.
Our study explores how ecological aspects of motor learning enhance survival by improving movement efficiency and mitigating injury risks during task failures. Traditional motor control theories mainly address isolated body movements and often overlook these ecological factors. We introduce a novel computational motor ...	Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning.
As generative artificial intelligence (AI) rapidly transforms educational landscapes, understanding its impact on students' core competencies has become increasingly critical for educators and policymakers. Despite growing integration of AI technologies in classrooms, there remains a significant knowledge gap regarding...	Generative artificial intelligence in secondary education: Applications and effects on students' innovation skills and digital literacy.
Allopregnanolone, an endogenous neurosteroid that is a potent, positive allosteric modulator of γ-aminobutyric acid type A (GABA<sub>A</sub>) receptors, has emerged as a compound with considerable potential in the treatment of psychiatric disorders, including substance use disorders and postpartum depression. We previo...	Allopregnanolone Regulation of Phasic Dopamine Release and Motivated Behavior.
Recent advances in deep reinforcement learning (DRL) have expanded its use in various automation sectors, including the nuclear industry. While DRL shows promise for optimizing radiation exposure, the development of radiation-aware autonomous unmanned aerial vehicles (UAVs) is hindered by inefficient reward functions a...	RadDQN: A Deep Q Learning-Based Architecture for Finding Time-Efficient Minimum Radiation Exposure Pathway.
Temporal difference (TD) learning is a fundamental technique in reinforcement learning that updates value function estimates for states or state-action pairs using a TD target. This target represents an improved estimate of the true value by incorporating both immediate rewards and the estimated value of subsequent sta...	Multistate Temporal Difference Target for Model-Free Reinforcement Learning.
Ambivalent professional identity (API)-the coexistence of identification and dis-identification with one's profession-undermines motivation and satisfaction. Research on API among medical interns remains limited. This study applies social cognitive theory (SCT) to explore the formation mechanism of API in Chinese medic...	Investigation of the mechanism of ambivalent professional identity formation: A directed content analysis of reflective assignments.
Integrated understanding of pharmacokinetics (PK) and pharmacodynamics (PD) is a key aspect of successful drug discovery. Yet in generative computational drug design, the focus often lies on optimizing potency. Here we integrate PK property predictions in DrugEx, a generative drug design framework and we explore the ge...	Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design.
Psychosis spectrum illnesses are characterized by impaired goal-directed behavior and significant neurophysiological heterogeneity. To investigate the neurocomputational underpinnings of this heterogeneity, 75 participants with Early Psychosis (EP) and 68 controls completed a dynamic decision-making task. Consistent wi...	Beyond reward learning deficits: Exploration-exploitation instability reveals computational heterogeneity in value-based decision making in early psychosis.
Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessib...	MovementVR: An open-source tool for the study of motor control and learning in virtual reality.
Perfume flowers provide chemicals as a resource for specialized pollinators, that is, male euglossine bees. This system has been recorded in at least 15 families of Neotropical Angiosperms, including species of the bromeliad genus Cryptanthus. Here, we investigated the pollination and chemical ecology of Cryptanthus ba...	Nectar or perfume as reward? Investigating the pollination and chemical ecology of the bromeliad Cryptanthus bahianus.
Sexual activity produces pleasure related to sexual arousal, desire, and genitosensory and erogenous stimulation. Orgasms produce a whole brain and body rush of ecstatic pleasure followed by relaxation and refractoriness. This pleasure results from the activation of neurochemical reward pathways in the brain. This is d...	Orgasms, sexual pleasure, and opioid reward mechanisms.
The efficiency optimization methods for natural coagulants are often restricted due to non-scientific trial-and-error approaches. They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. To overcome these obstacles, this research paper proposes...	Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment.
Dogs show numerousness, which is the ability to identify the larger of two stimuli, most often the number of treats on a plate. However, dogs seem to use mechanisms other than counting to make this discrimination. This study builds on existing research by controlling for (a) olfaction, (b) the surface area of the stimu...	Training numerousness to numerosity in the dog (Canis lupus familiaris).
Sequential decision-making often involves a combination of simple trial-and-error learning (i.e., model-free learning), and more sophisticated learning where an abstract representation of the environment is formed, thereby facilitating prospective predictions about likely outcomes based on different choices (i.e., mode...	Physical Activity Is Positively Associated with Model-Based Decision Making in Pursuit of Reward in Trauma-Exposed Adults.
Reward-associated cues guide reward-seeking behaviours. These cues include conditioned stimuli (CSs), which occur following seeking actions and predict reward delivery, and discriminative stimuli (DSs), which occur response-independently and signal that a seeking action will produce reward. Metabotropic group II glutam...	Activating Group II Metabotropic Glutamate Receptors in the Basolateral Amygdala Inhibits Increases in Reward Seeking Triggered by Discriminative Stimuli in Rats.
In response to the challenges of false positives and misses caused by dense occlusions and small targets in complex road environments, this paper proposes an enhanced YOLOv8-based network named YOLO-RC for advanced road traffic object detection. YOLO-RC utilizes MBConv modules to enhance feature extraction in the backb...	A complex roadside object detection model based on multi-scale feature pyramid network.
Leadership is essential for nursing to provide high-quality treatment, productive cooperation, and flexible responses to the always-developing healthcare environment. In nursing educational programs, entrepreneurial leadership places a strong emphasis on cultivating qualities that promote creative problem-solving and i...	Building nursing leaders: the influence of entrepreneurial leadership program on nurse interns' innovation and clinical performance.
Knee osteoarthritis (KOA) is a prevalent chronic disease worldwide, and traditional treatment methods lack personalized adjustment for individual patient differences and cannot meet the needs of personalized treatment. In this study, a dedicated knee osteoarthritis bank (KOADB) was constructed by collecting extensive c...	Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning.
Nonhuman primates (NHPs) are pivotal for unlocking the complexities of human cognition, yet traditional cognitive studies remain constrained to specialized laboratories. To address this gap, we present CalliCog: an open-source, scalable in-cage platform tailored for experiments in small freely behaving primate species ...	CalliCog is an open-source cognitive neuroscience toolkit for freely behaving nonhuman primates.
In this issue of Neuron, Pignatelli et al.<sup>1</sup> find that ketamine reverses stress-induced changes in excitatory synapses in nucleus accumbens D1 dopamine receptor-expressing medium spiny neurons (D1-MSNs) and that these changes are necessary for the treatment of anhedonia-like behavior.	Breaking through anhedonia: How ketamine reignites the drive for rewards.
What is the motivational force of the sense of obligation that drives us to intrinsically comply with social norms even in the absence of external incentives? To integrate recent theoretical and empirical research aiming to illuminate the motivational power of psychological obligations, we combine the theory of basic p...	Intrinsically motivated norm compliance and the sense of obligation.
Single-cell organisms and various cell types use a range of motility modes when following a chemical gradient, but it is unclear which mode is best suited for different gradients. Here, we model directional decision-making in chemotactic amoeboid cells as a stimulus-dependent actin recruitment contest. Pseudopods exten...	Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients.
It is common to say that people feel entitled to rewards-they think they have earned or deserve them-based on their effort and achievement. However, effort and achievement draw on different principles to justify reward. They can also conflict over when people should feel entitled to rewards. These observations raise th...	Achievement (not effort) makes people feel entitled to rewards.

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