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Introduction Large language model (LLM)-linked chatbots are being increasingly applied in healthcare due to their impressive functionality and public availability. Studies have assessed the ability of LLM-linked chatbots to provide accurate clinical advice. However, the methods applied in these Chatbot Assessment Studies are inconsistent due to the lack of reporting standards available, which obscures the interpretation of their study findings. This protocol outlines the development of the Chatbot Assessment Reporting Tool (CHART) reporting guideline.Methods and analysis The development of the CHART reporting guideline will consist of three phases, led by the Steering Committee. During phase one, the team will identify relevant reporting guidelines with artificial intelligence extensions that are published or in development by searching preprint servers, protocol databases, and the Enhancing the Quality and Transparency of health research Network. During phase two, we will conduct a scoping review to identify studies that have addressed the performance of LLM-linked chatbots in summarising evidence and providing clinical advice. The Steering Committee will identify methodology used in previous Chatbot Assessment Studies. Finally, the study team will use checklist items from prior reporting guidelines and findings from the scoping review to develop a draft reporting checklist. We will then perform a Delphi consensus and host two synchronous consensus meetings with an international, multidisciplinary group of stakeholders to refine reporting checklist items and develop a flow diagram.Ethics and dissemination We will publish the final CHART reporting guideline in peer-reviewed journals and will present findings at peer-reviewed meetings. Ethical approval was submitted to the Hamilton Integrated Research Ethics Board and deemed “not required” in accordance with the Tri-Council Policy Statement (TCPS2) for the development of the CHART reporting guideline (#17025).Registration This study protocol is preregistered with Open Science Framework: https://doi.org/10.17605/OSF.IO/59E2Q.

Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems.

In spite of the capacity for the Internet to connect people and information irrespective of geography, physical location may paradoxically provide influential indicators of the perceived expertise of strangers and the credibility of the information they provide that may in turn guide people’s behaviors. To address this, this study examined the novel concept of geospatial concordance or the degree to which entities implicated in the sharing of aggregated opinions in online information pools are physically close to each other in geographic space. Predictions were tested in the context of user-generated online reviews using stimuli reflecting various types of geospatial concordance: between information consumers and online reviewers, between reviewed venues and their reviewers, and between consumers and reviewed venues. Findings support geographic perspectives emphasizing space as a mental construction imbued with particular meaning and confirm psychological views that people mentally construe places at different levels of abstraction, depending on their psychological, and physical, distance from them.

The human body has the ability to influence its sensation of pain by modifying the transfer of nociceptive information at the spinal level. This modulation, known as descending pain inhibition, is known to originate supraspinally and can be activated by a variety of ways including positive mental imagery. However, its exact mechanisms remain unknown. We investigated, using a longitudinal fMRI design, the brain activity leading up and in response to painful electrical stimulation when applying positive mental imagery before and after undergoing a previously established RIII-feedback paradigm. Time course analysis of the time preceding painful stimulation shows increased haemodynamic activity during the application of the strategy in the PFC, ACC, insula, thalamus, and hypothalamus. Time course analysis of the reaction to painful stimulation shows decreased reaction post-training in brainstem and thalamus, as well as the insula and dorsolateral PFC. Our work suggests that feedback training increases activity in areas involved in pain inhibition, while simultaneously decreasing the reaction to painful stimuli in brain areas related to pain processing, which points to an activation of decreased spinal nociception. We further suggest that the insula and the thalamus may play a more important role in pain modulation than previously assumed.

Abstract Research on digital communication is best served by concentrating not on current technological tools, but rather on the novel processes of social and communicative change to which such technologies are often closely linked. Thus, scholarship should consider contemporary technologies mainly as manifestations of underlying phenomena rather than as particular objects of study and should cultivate a focus on the capabilities that span technologies. Theories in this domain, in turn, must concentrate on what is exceptional about technologies in terms of the psychological, social, and behavioral mechanisms that help to comprehend them in the long term. Seen this way, the key to understanding technological mediation is remarkably consistent even in the face of radical technological change.

Deep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks in levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network to detect cracks from levee images at a pixel level automatically. We propose that the feature learning be strengthened using the decoder and bottleneck feature maps by concatenating them back to the encoder blocks. The addition reinforcement in the U-Net-like architecture results in a loop-like structure to exploit all the feature maps from encoders, bottlenecks, and decoders. The proposed architecture, Iterative Loop U-Net (IterLUNet), outperforms the state-of-the-art architectures on the image dataset of the levee system, achieving an increment of Intersection over Union (IoU) by 10.32% on average for a 10-Fold Cross-Validation (FCV) compared to the baseline U-Net model and 11.00%, 7.65%, and 7.43% with a range of latest models MultiResUnet, Attention U-Net, and Unet++ respectively. In addition, IterLUNet has at least 63% fewer parameters to be trained than the baseline model, thus, allowing less space consumption for pixel-wise crack detection in AI-based inspection of levee systems.

With advances in technology, artificial agents such as humanoid robots will soon become a part of our daily lives. For safe and intuitive collaboration, it is important to understand the goals behind their motor actions. In humans, this process is mediated by changes in activity in fronto-parietal brain areas. The extent to which these areas are activated when observing artificial agents indicates the naturalness and easiness of interaction. Previous studies indicated that fronto-parietal activity does not depend on whether the agent is human or artificial. However, it is unknown whether this activity is modulated by observing grasping (self-related action) and pointing actions (other-related action) performed by an artificial agent depending on the action goal. Therefore, we designed an experiment in which subjects observed human and artificial agents perform pointing and grasping actions aimed at two different object categories suggesting different goals. We found a signal increase in the bilateral inferior parietal lobule and the premotor cortex when tool versus food items were pointed to or grasped by both agents, probably reflecting the association of hand actions with the functional use of tools. Our results show that goal attribution engages the fronto-parietal network not only for observing a human but also a robotic agent for both self-related and social actions. The debriefing after the experiment has shown that actions of human-like artificial agents can be perceived as being goal-directed. Therefore, humans will be able to interact with service robots intuitively in various domains such as education, healthcare, public service, and entertainment.

All volitional movement in a three-dimensional space requires multisensory integration, in particular of visual and vestibular signals. Where and how the human brain processes and integrates self-motion signals remains enigmatic. Here, we applied visual and vestibular self-motion stimulation using fast and precise whole-brain neuroimaging to delineate and characterize the entire cortical and subcortical egomotion network in a substantial cohort (n=131). Our results identify a core egomotion network consisting of areas in the cingulate sulcus (CSv, PcM/pCi), the cerebellum (uvula), and the temporo-parietal cortex including area VPS and an unnamed region in the supramarginal gyrus. Based on its cerebral connectivity pattern and anatomical localization, we propose that this region represents the human homologue of macaque area 7a. Whole-brain connectivity and gradient analyses imply an essential role of the connections between the cingulate sulcus and the cerebellar uvula in egomotion perception. This could be via feedback loops involved updating visuo-spatial and vestibular information. The unique functional connectivity patterns of PcM/pCi hint at central role in multisensory integration essential for the perception of self-referential spatial awareness. All cortical egomotion hubs showed modular functional connectivity with other visual, vestibular, somatosensory and higher order motor areas, underlining their mutual function in general sensorimotor integration.

Estimates of the duration of time intervals and other magnitudes exhibit characteristic biases that likely result from error minimization strategies. To investigate such phenomena, magnitude reproduction tasks are used with humans and other primates. However, such behavioral tasks do not exist for rodents, one of the most important animal orders for neuroscience. We, therefore, developed a time reproduction task that can be used with rodents. It involves an animal reproducing the duration of a timed visual stimulus by walking along a corridor. The task was implemented in virtual reality, which allowed us to ensure that the animals were actually estimating time. The hallway did not contain prominent spatial cues and movement could be de-correlated from optic flow, such that the animals could not learn a mapping between stimulus duration and covered distance. We tested the reproduction of durations of several seconds in three different stimulus ranges. The gerbils reproduced the durations with a precision similar to experiments on humans. Their time reproductions also exhibited the characteristic biases of magnitude estimation experiments. These results demonstrate that our behavioral paradigm provides a means to study time reproduction in rodents.

Objective: Strong magnetic fields from magnetic resonance (MR) scanners induce a Lorentz force that contributes to vertigo and persistent nystagmus. Prior studies have reported a predominantly horizontal direction for healthy subjects in a 7 Tesla (T) MR scanner, with slow phase velocity (SPV) dependent on head orientation. Less is known about vestibular signal behavior for subjects in a weaker, 3T magnetic field, the standard strength used in the Human Connectome Project (HCP). The purpose of this study is to characterize the form and magnitude of nystagmus induced at 3T. Methods: Forty-two subjects were studied after being introduced head-first, supine into a Siemens Prisma 3T scanner. Eye movements were recorded in four separate acquisitions over 20 min. A biometric eye model was fitted to the recordings to derive rotational eye position and then SPV. An anatomical template of the semi-circular canals was fitted to the T2 anatomical image from each subject, and used to derive the angle of the B0 magnetic field with respect to the vestibular apparatus. Results: Recordings from 37 subjects yielded valid measures of eye movements. The population-mean SPV ± SD for the horizontal component was -1.38 ± 1.27 deg/sec, and vertical component was -0.93 ± 1.44 deg/sec, corresponding to drift movement in the rightward and downward direction. Although there was substantial inter-subject variability, persistent nystagmus was present in half of subjects with no significant adaptation over the 20 min scanning period. The amplitude of vertical drift was correlated with the roll angle of the vestibular system, with a non-zero vertical SPV present at a 0 degree roll. Interpretation: Non-habituating vestibular signals of varying amplitude are present in resting state data collected at 3T.

Watching an instructional video has become a common way to learn a new task. However, we have but a sparse understanding of the neural processes involved during observational learning in naturalistic settings. Recently developed data driven methods for analyzing brain activity provide an opportunity for further investigation. Here, we evaluate intersubject synchrony during fMRI to understand common brain processes during naturalistic observational learning. Participants solitarily watched an instructional video and learned how to fold a paper figure. Three learning runs were sufficient to successfully solve the task. To assess interbrain synchrony, we extended previous principal component (PCA)-based methods to an intersubject principal component analysis (PCA), which offers multiple measures for additional insights into the nature of the synchrony. Using the different metrics of this method, we show a robust synchronous involvement of the action observation execution network (AOEN) in observational learning, between subjects as well as within subjects, regardless of the task or video content. Importantly, additional areas such as the cerebellum, primary motor cortex, control, and sensory integration areas also showed robust synchrony in observational learning. Complimentary to this robust general synchrony, individual regions of the AOEN exhibited task-related differences. Synchrony decreased during the learning process, likely reflecting task state and individual learning strategies. To test the stimulus as a possible source of synchrony, we quantified the temporal structure as the optic flow of the instructional video. Optic flow was strongly related to common activation of the somatomotor areas of the AOEN well beyond visual areas, but could not completely explain synchrony. Thus, although visual motion provides a proxy for meaningful hand actions, our results suggest that intersubject synchrony reflects common cognitive processing during observational learning beyond sensory input.

This document provides guidance on the use of artificial intelligence (AI), natural language processing (NLP), and language models (LLMs) in scholarly publishing. It emphasizes the importance of transparency, responsibility, and accountability in the use of AI to ensure the integrity and quality of published research. Authors are advised to disclose their use of AI tools and provide detailed information about their usage. Peer reviewers are cautioned against using AI tools that may compromise confidentiality, and editors are responsible for effectively and transparently implementing AI tools. Efforts should be made to bridge the digital divide and prevent disparities in access to AI technologies. Data protection measures, intellectual property rights, and addressing AI-induced academic misconduct are also highlighted as important considerations. [Extracted from the article]

Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems. [ABSTRACT FROM AUTHOR]

Research typically presumes that people believe misinformation and propagate it through their social networks. Yet, a wide range of motivations for sharing misinformation might impact its spread, as well as people’s belief of it. By examining research on motivations for sharing news information generally, and misinformation specifically, we derive a range of motivations that broaden current understandings of the sharing of misinformation to include factors that may to some extent mitigate the presumed dangers of misinformation for society. To illustrate the utility of our viewpoint we report data from a preliminary study of people’s dis/belief reactions to misinformation shared on social media using natural language processing. Analyses of over 2,5 million comments demonstrate that misinformation on social media is often disbelieved. These insights are leveraged to propose directions for future research that incorporate a more inclusive understanding of the various motivations and strategies for sharing misinformation socially in large-scale online networks.

Abstract Brain atlases and templates are core tools in scientific research with increasing importance also in clinical applications. Advances in neuroimaging now allowed us to expand the atlas domain to the vestibular and auditory organ, the inner ear. In this study, we present IE-Map, an in-vivo template and atlas of the human labyrinth derived from multi-modal high-resolution magnetic resonance imaging (MRI) data, in a fully non-invasive manner without any contrast agent or radiation. We reconstructed a common template from 126 inner ears (63 normal subjects) and annotated it with 94 established landmarks and semi-automatic segmentations of all relevant macroscopic vestibular and auditory substructures. We validated the atlas by comparing MRI templates to a novel CT/micro-CT atlas, which we reconstructed from 21 publicly available post-mortem images of the bony labyrinth. Templates in MRI and micro-CT have a high overlap, and several key anatomical measures of the bony labyrinth in IE-Map are in line with micro-CT literature of the inner ear. A quantitative substructural analysis based on the new template, revealed a correlation of labyrinth parameters with total intracranial volume. No effects of gender or laterality were found. We provide the validated templates, atlas segmentations, surface meshes and landmark annotations as open-access material, to provide neuroscience researchers and clinicians in neurology, neurosurgery, and otorhinolaryngology with a widely applicable tool for computational neuro-otology.