Close to the metal: Towards a material political economy of the epistemology of computation

This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of both cryptoasset mining and machine learning models. The political economy associated with video games and Bitcoin and Ethereum mining provided a staggering growth in performance and energy efficiency and this, in turn, fostered a change in the epistemological understanding of AI: from rules-based or symbolic AI towards the matrix multiplications underpinning connectionism, machine learning and neural nets. Combining a material political economy of markets with a material epistemology of science, the article shows that there is no clear-cut division between software and hardware, between instructions and tools, and between frameworks of thought and the material and economic conditions of possibility of thought itself. As the microchip shortage and the growing geopolitical relevance of the hardware and semiconductor supply chain come to the fore, the paper invites social scientists to engage more closely with the materialities and hardware architectures of 'virtual' algorithms and software.

Bibliometric review of carbon peak with CiteSpace: evolution, trends, and framework

In the context of global climate change, countries around the world are actively implementing carbon peak and carbon neutrality goals. In-depth research on carbon peak can help improve environmental conditions and achieve a harmonious coexistence between economic development and environmental protection. However, a comprehensive review of the current status of research in this area is scarce. Therefore, this article explores the current research evolution and hotspots of "carbon peak" with the help of CiteSpace visualization software, predicts the future development trends, and builds a knowledge network framework. A comprehensive analysis of the research on carbon peak from multiple perspectives is presented. The results show that the number of papers published on carbon peak is increasing every year, and that carbon peak has become a widely participated research area. Publications from various institutions and journals have also attracted widespread attention. The research hotspots of carbon peak have constantly changed with time, resulting in many theoretical and technological innovations. The knowledge framework of the field is constructed on this basis, which gives readers a clearer understanding of the development and trends in the field and provides some reference and help for future researchers.

Syntactic adaptation leads to updated knowledge for local structural frequencies

Syntactic adaptation has been shown to occur for various temporarily ambiguous structures, wherein an initially unexpected resolution becomes easier to process after repeated exposure. More controversial and less replicated is the claim that this adaptation towards a locally frequent structure occurs due to a strategic shifting of expectations to match short-term statistical regularities such that readers adapt away from the a priori more frequent structure. Experiment 1 replicates the initial adaptation towards a coordination garden path structure using self-paced reading; however, this paradigm has been criticised for its low reliability for detecting such small effects. To this end, Experiments 2 and 3 use a combination of self-paced reading and sentence completion tasks to replicate initial adaptation towards both coordination and reduced relative garden path structures and show evidence for a preference for these structures over their a priori more frequent alternatives. Together, these data reveal that participants may be tracking local structural statistics in real time; however, they may not be able to rapidly use that information to update processing behaviours.

The impact of implicit narrator reliability on production of information

Previous research established that readers acquire accurate and inaccurate information from fiction. The current study explored factors that might moderate these effects. Participants read fictional stories that each contained three assertions. The first two assertions in each story were either correct information or implausible misinformation, allowing a manipulation of the (implicit) credibility of the narrator. The last assertion in each story was the critical one, and was correct information, implausible misinformation, or plausible misinformation. After reading, participants answered general knowledge questions that were related to the critical assertions they encountered during reading. Encountering misinformation led to lower accuracy than being presented with correct information, and being presented with plausible misinformation led to higher production of that misinformation. The narrator credibility manipulation interacted with the type of critical assertion: When the critical assertion was presented accurately in a story, credible narrators presenting true critical assertions led to greater accuracy on the general knowledge test than when noncredible narrators presented this same information. These findings are discussed with respect to theories of validation during language comprehension.

Tacit knowledge and the role of the dental educator

INTRODUCTION

This article seeks to explore tacit knowledge in the context of the practice and the role of a dental educator in a workplace learning environment.

MATERIAL AND METHODS

The key theoretical ideologies which underpin the definition of tacit knowledge have been outlined and practical examples to enable conceptualisation. The role tacit knowledge plays in procedural knowledge, performance of a skill and diagnosis and decision-making has been explained in further detail. Approaches to maximise the educational output of learning opportunities by using tacit knowledge and how an awareness of tacit knowledge can complement reflection have been considered.

RESULTS

It is acknowledged that workplace learning is of mutual benefit to the dental educator, trainee and clinical team and that the development of the educator to make tacit knowledge explicit, can be achieved through peer observation, amongst other methods.

CONCLUSION

Tacit knowledge is a key element underpinning learning in the workplace; the use of this knowledge can be applied in an advantageous manner, from both an educational and a personal developmental perspective.

Teacher-student complementary sample contrastive distillation

Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques.

Examining ChatGPT Performance on USMLE Sample Items and Implications for Assessment

PURPOSE

In late 2022 and early 2023, reports that ChatGPT could pass the United States Medical Licensing Examination (USMLE) generated considerable excitement, and media response suggested ChatGPT has credible medical knowledge. This report analyzes the extent to which an artificial intelligence (AI) agent's performance on these sample items can generalize to performance on an actual USMLE examination and an illustration is given using ChatGPT.

METHOD

As with earlier investigations, analyses were based on publicly available USMLE sample items. Each item was submitted to ChatGPT (version 3.5) 3 times to evaluate stability. Responses were scored following rules that match operational practice, and a preliminary analysis explored the characteristics of items that ChatGPT answered correctly. The study was conducted between February and March 2023.

RESULTS

For the full sample of items, ChatGPT scored above 60% correct except for one replication for Step 3. Response success varied across replications for 76 items (20%). There was a modest correspondence with item difficulty wherein ChatGPT was more likely to respond correctly to items found easier by examinees. ChatGPT performed significantly worse ( P < .001) on items relating to practice-based learning.

CONCLUSIONS

Achieving 60% accuracy is an approximate indicator of meeting the passing standard, requiring statistical adjustments for comparison. Hence, this assessment can only suggest consistency with the passing standards for Steps 1 and 2 Clinical Knowledge, with further limitations in extrapolating this inference to Step 3. These limitations are due to variances in item difficulty and exclusion of the simulation component of Step 3 from the evaluation-limitations that would apply to any AI system evaluated on the Step 3 sample items. It is crucial to note that responses from large language models exhibit notable variations when faced with repeated inquiries, underscoring the need for expert validation to ensure their utility as a learning tool.

Abstract concepts and simulated competition

To better understand the social determinants of conceptual knowledge we devised a task in which participants were asked to judge the match between a definition (expressed in abstract or concrete terms) and a target-word (also either abstract or concrete). The task was presented in the form of a competition that could/could not include an opponent, and in which different percentages of response rounds were assigned to the participant at the experimenter's discretion. Thus, depending on the condition, participants were either exposed to a competitive context mimicking a privileged/unprivileged interaction with the experimenter or to a socially neutral setting. Results showed that manipulation of the social context selectively affected judgments on abstract stimuli: responses were significantly slower whenever a definition and/or a target word were presented in abstract form and when participants were in the favorable condition of responding in most of the trials. Moreover, only when processing abstract material, responses were slower when an opponent was expected to be present. Data are discussed in the frame of the different cognitive engagements involved when treating abstract and concrete concepts as well as in relation to the possible motivational factors prompted by the experimental set-up. The role of social context as a crucial element for abstract knowledge processing is also considered.

Evaluation of the Uniform Data Set version 3 teleneuropsychological measures

OBJECTIVE

Few studies have evaluated in-home teleneuropsychological (teleNP) assessment and none, to our knowledge, has evaluated the National Alzheimer's Coordinating Center's (NACC) Uniform Data Set version 3 tele-adapted test battery (UDS v3.0 t-cog). The current study evaluates the reliability of the in-home UDS v3.0 t-cog with a prior in-person UDS v3.0 evaluation.

METHOD

One hundred and eighty-one cognitively unimpaired or cognitively impaired participants from a longitudinal study of memory and aging completed an in-person UDS v3.0 and a subsequent UDS v3.0 t-cog evaluation (∼16 months apart) administered either via video conference (n = 122) or telephone (n = 59).

RESULTS

We calculated intraclass correlation coefficients (ICCs) between each time point for the entire sample. ICCs ranged widely (0.01-0.79) but were generally indicative of "moderate" (i.e., ICCs ranging from 0.5-0.75) to "good" (i.e., ICCs ranging from 0.75-0.90) agreement. Comparable ICCs were evident when looking only at those with stable diagnoses. However, relatively stronger ICCs (Range: 0.35-0.87) were found between similarly timed in-person UDS v3.0 evaluations.

CONCLUSIONS

Our findings suggest that most tests on the UDS v3.0 t-cog battery may serve as a viable alternative to its in-person counterpart, though reliability may be attenuated relative to the traditional in-person format. More tightly controlled studies are needed to better establish the reliability of these measures.

Towards richer knowledge partnerships between ecology and ethnoecology

Indigenous and traditional practices based on ethnoecological knowledge are fundamental to biodiversity stewardship and sustainable use. Knowledge partnerships between Indigenous Peoples, traditional local communities, and ecologists can produce richer and fairer understandings of nature. We identify key topical areas where such collaborations can positively transform science, policy, and practice.

In-Distribution and Out-of-Distribution Self-Supervised ECG Representation Learning for Arrhythmia Detection

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area. We then perform a comprehensive set of experiments using different augmentations and parameters to evaluate the effectiveness of various SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning, where we observe the best performance achieved by SwAV. Furthermore, our analysis shows that SSL methods achieve highly competitive results to those achieved by supervised state-of-the-art methods. To further assess the performance of these methods on both In-Distribution (ID) and Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and testing experiments. Our comprehensive experiments show almost identical results when comparing ID and OOD schemes, indicating that SSL techniques can learn highly effective representations that generalize well across different OOD datasets. This finding can have major implications for ECG-based arrhythmia detection. Lastly, to further analyze our results, we perform detailed per-disease studies on the performance of the SSL methods on the three datasets.

In Commemoration of Dr. Mostafa Pourtaghva Shahrestani, a Pioneer in Infectious Disease Research

It is important to honor the contributions of scientific leaders who have dedicated their lives to advancing knowledge and serving their country. One way is to document their experiences and personalities in a documentary format, which can serve as a historical record and an inspiration for future generations. Dr. Mostafa Pourtaghva Shahrestani, a renowned physician and specialist in infectious diseases and tropical medicine, has made significant contributions to public health in Iran. He has played a crucial role in controlling infectious diseases such as smallpox, tuberculosis, rabies, plague, and cholera. Throughout his career, he has held various executive positions, including the head of Pasteur Hospital and the director of the Pasteur Institute of Iran. Dr. Pourtaghva's life is a testament to his unwavering dedication to public health services, as evidenced by his continuous effort, love, and interest in honest work. His inspiring story can serve as a model for those who seek to follow in his footsteps.

Animism and science in European perspective

The European tradition makes a sharp distinction between animism and science. On the basis of this distinction, either animism is reproved for failing to reach the heights of science, or science is reproved for failing to reach the heights of animism. In this essay, I draw on work in the history and philosophy and science, combined with a method from the sociology of scientific knowledge, to question the sharpness of this distinction. Along the way, I also take guidance from the research of North American Indigenous scholars. As it turns out, there is a rich, if largely overlooked, tradition of Aristotelian animism running through the history of modern European science, and this tradition sometimes resonates with Indigenous perspectives. By challenging the entrenched distinction between animism and science, I aim to help reconcile ongoing tensions between Indigenous and European scientific groups, and so strengthen prospects for their mutually beneficial cooperation.

Progressing, not regressing: A possible solution to the problem of regression to the mean in unconscious processing studies

How convincing is current evidence for unconscious processing? Recently, a major criticism suggested that some, if not much, of this evidence might be explained by a mere statistical phenomenon: regression to the mean (RttM). Excluding participants based on an awareness assessment is a common practice in studies of unconscious processing, and this post hoc data selection might lead to false effects that are driven by RttM for aware participants wrongfully classified as unaware. Here, we examined this criticism using both simulations and data from 12 studies probing unconscious processing (35 effects overall). In line with the original criticism, we confirmed that the reliability of awareness measures in the field is concerningly low. Yet, using simulations, we showed that reliability measures might be unsuitable for estimating error in awareness measures. Furthermore, we examined other solutions for assessing whether an effect is genuine or reflects RttM; all suffered from substantial limitations, such as a lack of specificity to unconscious processing, lack of power, or unjustified assumptions. Accordingly, we suggest a new nonparametric solution, which enjoys high specificity and relatively high power. Together, this work emphasizes the need to account for measurement error in awareness measures and evaluate its consequences for unconscious processing effects. It further suggests a way to meet the important challenge posed by RttM, in an attempt to establish a reliable and robust corpus of knowledge in studying unconscious processing.

Knowing "what for," but not "where": Dissociation between functional and contextual tool knowledge in healthy individuals and patients with dementia

OBJECTIVE

Semantic tool knowledge underlies the ability to perform activities of daily living. Models of apraxia have emphasized the role of functional knowledge about the action performed with tools (e.g., a hammer and a mallet allow a "hammering" action), and contextual knowledge informing individuals about where to find tools in the social space (e.g., a hammer and a mallet can be found in a workshop). The goal of this study was to test whether contextual or functional knowledge, would be central in the organization of tool knowledge. It was assumed that contextual knowledge would be more salient than functional knowledge for healthy controls and that patients with dementia would show impaired contextual knowledge.

METHODS

We created an original, open-ended categorization task with ambiguity, in which the same familiar tools could be matched on either contextual or functional criteria.

RESULTS

In our findings, healthy controls prioritized a contextual, over a functional criterion. Patients with dementia had normal visual categorization skills (as demonstrated by an original picture categorization task), yet they made less contextual, but more functional associations than healthy controls.

CONCLUSION

The findings support a dissociation between functional knowledge ("what for") on the one hand, and contextual knowledge ("where") on the other hand. While functional knowledge may be distributed across semantic and action-related factors, contextual knowledge may actually be the name of higher-order social norms applied to tool knowledge. These findings may encourage researchers to test both functional and contextual knowledge to diagnose semantic deficits and to use open-ended categorization tests.

Patterns and Pitfalls of Short-cuts Used in Environmental Management Rapid Reviews

Environmental managers and policy-makers need reliable evidence to make effective decisions. Systematic reviews are one way to provide this information but are time-consuming and may not meet the needs of decision-makers when faced with rapidly changing management requirements or transient policy-windows. Rapid reviews are one type of knowledge synthesis that follow simplified or truncated methods compared to systematic reviews. Rapid reviews on environmentally-relevant topics are growing in prevalence, but it is unclear if rapid reviews use similar short-cuts or follow available guidelines. In this methodological review, we assess 26 rapid reviews published between 2002 and 2023. Numerous rapid review short-cuts and approaches were identified, with few consistencies among studies. Short-cuts were present in all stages of the review process, with some of the most common short-cuts including not developing an a priori review protocol, not including stakeholder involvement, or not conducting critical appraisal of study validity. Poor quality in reporting of methods was observed. Fewer than half of assessed rapid reviews reported using available guidelines when developing their methods. Future rapid reviews should aim for improved reporting and adherence to published guidelines to help increase the useability and evidence-user confidence. This will also enable readers to understand where short-cuts were made and their potential consequences for the conclusions of the review.

Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving

Heterogeneous domain adaptation (HDA) methods leverage prior knowledge from the source domain to train models for the target domain and address the differences in their feature spaces. However, incorrect alignment of categories and distribution structure disruption may be caused by unlabeled target samples during the domain alignment process for most existing methods, resulting in negative transfer. Additionally, the previous works rarely focus on the robustness and interpretability of the model. To address these issues, we propose a novel Graph embedding-based Heterogeneous domain-Invariant feature learning and Distributional order preserving framework (GHID). Specifically, a bidirectional robust cross-domain alignment graph embedding structure is proposed to globally align two domains, which learns the domain-invariant and discriminative features simultaneously. In addition, the interpretability of the proposed graph structures is demonstrated through two theoretical analyses, which can elucidate the correlation between important samples from a global perspective in heterogeneous domain alignment scenarios. Then, a heterogeneous discriminative distributional order preserving graph embedding structure is designed to preserve the original distribution relationship of each domain to prevent negative transfer. Moreover, the dynamic centroid strategy is incorporated into the graph structures to improve the robustness of the model. Comprehensive experimental results on four benchmarks demonstrate that the proposed method outperforms other state-of-the-art approaches in effectiveness.

Critical voxel learning with vision transformer and derivation of logical AV safety assessment scenarios

Safety assessment is an active research subject for autonomous vehicles (AVs) that have emerged as a new mode of mobility. In particular, scenario-based safety assessments have garnered significant attention. AVs can be tested on how they safely avoid hypothetical situations leading to accidents. However, scenarios written by humans based on their expert knowledge and experience may only partially reflect real-world situations. Instead, we are keen on a different technique of extracting statistically significant and more detailed scenarios from sensor data captured during the critical moments when AVs become vulnerable to potential accidents. Specifically, we first render the three-dimensional space around an AV with fixed-sized voxels. Then, we modeled the aggregate kinetics of the objects in each voxel detected by 3D-LiDAR sensors mounted on real test AVs. The Vision Transformer we used to model the kinetics helped us quickly pinpoint critical voxels containing objects that threatened the AV's safety. We traced the trajectory of the critical voxels on a visual attention map to describe in detail how AVs become vulnerable to accidents according to the logical scenario format defined by the PEGASUS Project. We tested our novel method with 250 h of 3D-LiDAR recordings capturing critical moments. We devised an inference model that detected critical situations with an F1-score of 98.26%. For each type of scenario, our model consistently identified the critical objects and their tendency to influence AVs. Given the evaluation results, we can ensure that our data-driven approach yields an AV safety assessment scenario with high representativeness, coverage, expansion, and computational feasibility.

Dialectical Pluralism for Nursing Knowledge Development

The purpose of this paper is to explore the potential of dialectical pluralism (DP) for nursing knowledge development. Nursing scholars have discussed ways of developing nursing knowledge, exploring the fit and relevance of various worldviews for knowledge development and examining the dynamic and perpetual processes of knowledge development. Scholars have argued that knowledge development occurs under a certain worldview to which the researcher adheres. Many nurses employ various worldviews, which can give rise to ontological and epistemological conflicts. DP can help nurses appreciate the diversity of worldviews and recognize the importance of implicit worldviews to generate more practical nursing knowledge. DP as a philosophical approach can enable nurses to communicate between diverse worldviews, become tolerant of conflicting differences, and develop an array of nursing knowledge.

Exploring the potential of waste plastic-modified asphalt: a systematic review of blending ratios, mixing conditions, and rheological properties

In the present study, a systematic literature review (SLR) is conducted to collect, compile, and summarize the findings of previous studies in a meaningful and systematic way. This review focuses on the ideal blending ratios, mixing parameters, and the physical, thermal, and rheological performance of waste plastic-modified asphalt. It highlights the most significant research results about the challenges like phase separation, low-temperature performance, and workability for waste plastic-modified asphalt and progress in this domain. The results point out that the use of chemical and physical additives can help in the reduction of phase separation. Furthermore, this paper debates the aging characteristics and it was seen that the integration of waste plastic in asphalt has shown to slow down the aging process of the binder. The review article put forward details of various field projects across the globe utilizing waste plastic. The review concludes by presenting key findings, identifying research gaps, and suggesting future directions to advance the knowledge and to fully comprehend the possible application of waste plastic-modified bitumen in sustainable road construction.

Technological Encounters in a Knowledge Economy: An Epistemic X-Ray

Climate emergency is a planetary health and systems science challenge because human health, nonhuman animal health, and the health of the planetary ecosystems are coproduced and interdependent. Yet, we live in a time when climate emergency is tackled by platitudes and weak reforms instead of structural and systems changes, and with tools of the very same systems and metanarratives, for example, infinite growth at all costs, that are causing climate change in the first place. Seeking solutions to problems from within the knowledge frames and metanarratives that are causing the problems reproduces the same problems across time and geographies. This article examines and underlines the importance of an epistemological gaze on knowledge economy, an epistemological X-ray, as another solution in the toolbox of decolonial and other social justice struggles in an era of climate emergency. Epistemology questions and excavates the metanarratives embedded in knowledge forms that are popular, dominant, and hegemonic as well as knowledges that are silent, omitted, or erased. In this sense, epistemology does not take the "archives" of data and knowledge for granted but asks questions such as who, when, how, and with what and whose funding the archive was built, and what is included and left out? Epistemological choices made by innovators, funders, and knowledge actors often remain opaque in knowledge economies. Epistemology research is crucial for science and innovations to be responsive to planetary society and climate emergency and mindful of the social, political, neocolonial, and historical contexts of science and technology in the 21st century.

Knowledge mapping on the sun-induced chlorophyll fluorescence technology research: a scientometric and visualization analysis

The sun-induced chlorophyll fluorescence (SIF) has received increasing attention over the past few years. This scientometric study analyzed the SIF research field, based on publications available in Web of Science Core Collection visualization, and cluster analysis enabled us to map the knowledge domain and intellectual landscape of this field and identify thematic trends, landmark articles, and emerging research themes. In this study, the software VOSviewer and CiteSpace were used to identify the intellectual base and research front using visualization and analysis. Then, we analyzed synthesized networks of co-authorship (author, institution, and country), co-citation (author, document, and journal), and co-occurring keywords. SIF has increased its publication output steadily since 2002. This study provided a visual knowledge map of the major research domains of SIF. Also, explanations and implications of the findings were explored, and the emerging trends were identified. This report provides a thorough overview of the current state and growth trends of SIF research, and it helps researchers and practitioners understand the major areas of attention in this area and provides insights into prospective areas for further study.

[Treatment of pleural space infections]

Pleural space infections occur in approximately 10% of patients hospitalized for pneumonia and their incidence is increasing with an aging population. Pulmonary ultrasound is a good bedside diagnostic tool able to reduce complications associated with thoracocentesis and drainage. The RAPID score is being increasingly validated as a predictor for mortality but has not yet been proven useful to guide the treatment strategy and needs incorporation into prospective studies. Awaiting studies on outcome prediction and the comparison of an early surgical strategy versus drainage with intrapleural enzyme therapy, this article presents a summary of current knowledge of this complication.

An evaluation of GPT models for phenotype concept recognition

OBJECTIVE

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.

MATERIALS AND METHODS

The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.

RESULTS

The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.

CONCLUSION

Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

[Scientific consensus-building to promote the link between science and public policy]

Consensus, defined as the position on which most scientists specialized in a given field agree at a given time, is a key aspect in increasing the readability, credibility and, ultimately, the use of scientific knowledge in public (evidence-based health policy). This article presents several methods aiming at developing scientific consensus between experts, such as the conventional or rapid Delphi approach, the nominal group technique, the RAND-UCLA appropriateness method and the consensus development conference. These methods are used to synthesize expert judgements when uncertainties persist in the literature - each with its own specificities in terms of duration, number of steps and expert participants enlisted, as well as the ways in which they are involved.

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction

BACKGROUND

The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.

RESULTS

In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.

CONCLUSIONS

Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.

Discovery of lipid binding sites in a ligand-gated ion channel by integrating simulations and cryo-EM

Ligand-gated ion channels transduce electrochemical signals in neurons and other excitable cells. Aside from canonical ligands, phospholipids are thought to bind specifically to the transmembrane domain of several ion channels. However, structural details of such lipid contacts remain elusive, partly due to limited resolution of these regions in experimental structures. Here, we discovered multiple lipid interactions in the channel GLIC by integrating cryo-electron microscopy and large-scale molecular simulations. We identified 25 bound lipids in the GLIC closed state, a conformation where none, to our knowledge, were previously known. Three lipids were associated with each subunit in the inner leaflet, including a buried interaction disrupted in mutant simulations. In the outer leaflet, two intrasubunit sites were evident in both closed and open states, while a putative intersubunit site was preferred in open-state simulations. This work offers molecular details of GLIC-lipid contacts particularly in the ill-characterized closed state, testable hypotheses for state-dependent binding, and a multidisciplinary strategy for modeling protein-lipid interactions.

Everything, everywhere, almost at once

A new platform that can follow the movement of individual proteins inside millions of cells in a single day will help contribute to existing knowledge of cell biology and identify new therapeutics.

Leader-follower formation control based on non-inertial frames for non-holonomic mobile robots

A chain formation strategy based on mobile frames for a set of n differential drive mobile robots is presented. Considering two consecutive robots in the formation, robots Ri and Ri+1. It is intended that robot Ri+1 follows the delayed trajectory, τ units of time, of the leader robot Ri. In this way, the follower robot Ri+1 becomes the leader robot for robot Ri+ 2 in the formation and so on. With this formation policy, the trailing distance between two consecutive robots varies accordingly to the velocity of the Ri leader robot. Mobile frames are located on the body of the vehicles, in such a way that the position of robot Ri is determined with respect to the frame located on Ri+1 robot. The strategy relies on the fact that the general leader robot R1 describes any trajectory generated by bounded linear v1(t) and angular ω1(t) velocities. For the remaining vehicles in the string, the strategy considers a desired trajectory for the follower robot Ri+1 obtained by an estimation of the delayed trajectory of the leader robot Ri. This desired estimated trajectory is obtained under the knowledge of the actual and past input velocities of the Ri robot. To formally prove the convergence of the formation strategy, the equations describing the time variation of the relative posture between any pair of consecutive vehicles in the formation are obtained, and a feedback law based on local measurements is proposed to get the convergence of robot Ri+1 to the delayed trajectory, τ units of time, of the trajectory previously described by robot Ri. Lyapunov techniques are considered for this fact. The effectiveness of the chain formation solution is evaluated by means of numerical simulations and real time experiments showing an adequate convergence.

Knowledge, attitudes and practices regarding the use of mobile travel health apps

We performed a survey of US international travellers to evaluate their knowledge, attitudes and practices regarding mobile technologies related to health. We found that many international travellers carry smartphones and are interested in receiving health information from a mobile app when they travel abroad.

A Personal Healthcare Knowledge Graph Framework for Diagnosis of Pelvic Masses Diseases

A Personal Health Knowledge Graph (PHKG) facilitates the efficient integration of potential diagnostic clues from patients' electronic health records with medical knowledge, establishing diagnostic reasoning paths and ensuring accurate, individually interpretable results in the diagnosis of pelvic masses.

Graph Neural Network Based Multi-Label Hierarchical Classification for Disease Predictions in General Practice

General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model's decision-making.

Community of Interoperability Labs: Pragmatic Approach to Achieving Interoperability

To achieve interoperability of health data, stakeholders must overcome various socio-technical challenges. The "Mind the GAPS, Fill the GAPS" framework was created by the Asia eHealth Information Network (AeHIN) in 2017 to help countries with their challenges with interoperability. A year later, AeHIN formed the Community of Interoperability Labs (COIL), a group of labs from six countries to share knowledge and resources. Since interoperability requires data exchange between disparate entities, it is imperative to establish a trustworthy space where stakeholders can come together and solve their common problems. The networked learning approach of the COIL makes possible the potential for interoperability within and between countries contributing to national and international understanding.

Developing an Appendicectomy Surgical Pathway Ontology (ASPO)

This study proposes an ontology model to represent the surgical pathways for appendicectomy, incorporating domain knowledge extracted from literature and electronic records in an Australian health district. The ontology comprised 108 concepts and 81 object properties. The model is useful for continuous quality improvement initiatives, i.e., process mining to understand what happens in the hospital versus what is required by the policy.

User Perceptions and Use of Decision Support Medical Apps Among Medical Students: Cross-Sectional Study

We aimed to assess medical students' use of decision-support medical apps and evaluate their perception of app use. A cross-sectional multi-center observational study was conducted among medical students with and without a medical informatics course as part of their undergraduate medical curriculum. We assessed trust, perceptions, patient impression, reliability, and comfort using an online survey. A total of 439 responses were received. There were significant differences between the two groups when indicating which apps, they trust. Students agreed that using apps enhanced knowledge (91%), saved time (88%), improved patient care (85%), and increased diagnostic accuracy (82%). Students indicated that patients would think that students didn't know what they were doing (63%) or students were fresh out of training (53%) when using apps in the presence of patients. Incorporating medical app usage as part of learning may increase trust and comfort with using medical apps in medical practice.

Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR

The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these methods and expert clinical knowledge. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data to analyse the concordance between these factors and discusses causes for identified discrepancies from a clinical and technical perspective. Important factors for achieving trustworthy XAI solutions for clinical decision support are also discussed.

Effectiveness of Clinical Management of COVID-19 Based on Structured Clinical Knowledge and Process Paths

This study examined the effectiveness of a systematic approach to the clinical management of COVID-19, focusing on nursing turnover.

METHODS

Between 2017 and 2019, a clinical process support system based on structured clinical knowledge (Team Compass with the Patient Condition Adaptive Path System; TC-PCAPS) was developed, and implemented in hospitals. In 2020, the COVID-19 clinical management system (COVID-19-CMS) was developed. In this study, the effectiveness of implementing both systems was analyzed. The analysis covered hospitals N, T, and B, where TC-PCAPS implementation started in 2019, 2020, and 2022, respectively. Data for the period from 2018 to 2022 were collected and compared.

RESULTS

Hospitals N and T implemented TC-PCAPS in the first year and the COVID-19-CMS in the following year. The nurse turnover rates of these hospitals were lower than those of the prefectures in which they were located. There was a trend towards a gradual reduction in nurse turnover. In contrast, hospital B, which had only just started to introduce these systems, saw a gradual increase in nurse turnover.

CONCLUSION

The data collected from these three hospitals suggested that this systematic approach has the potential to reduce nurse turnover, in addition to the previously reported ability of TC-PCAPS to reduce nurse overtime. In Japan, there is a need to respond to future pandemics and reform the work styles of physicians and nurses. The abovementioned systematic approach has great potential for contributing to both of these aims.

Reviewing Challenges in Specifying Interoperability Requirement in Procurement of Health Information Systems

Procurement of health information systems (HIS) is a complex and critical task that requires early identification of interoperability requirements. However, specifying adequate requirements is often associated with several challenges. We examined relevant peer-reviewed literature and public documents (policy documents, annual reports, and newspapers) to summarize existing challenges in specifying interoperability requirement during procurement of HISs. In this study, 32 public documents and 2343 peer-reviewed articles were found using Google search engine, Springer, PubMed and ScienceDirect. Collected data were analyzed using a thematic coding schema. Our result shows that challenges related to describing the needs properly, conflicting needs and knowledge gaps are shared between most articles. Further research in the direction of developing a model that can bridge knowledge gaps, facilitate interdisciplinary collaboration, and help to avoid fuzzy requirements is needed.

Lessons Learned from an Interprofessional European Summer School in Health Informatics

The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students' evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.

Providing Practical Knowledge and Skills to Handle Real-World Data? Lessons Learned from Med RWD Program

Both lectures and hands-on education are essential for the development of human resources that can use real-world data (RWD). The University of Tokyo has launched a new hybrid-style RWD educational program entitled "Medical Real World Data Utilization Human Resource Development Project" from FY2019 onwards. We present an overview of the overall picture of the project, including the development process of the educational program and the challenges associated with it.

Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study

Timely management of Chronic Obstructive Pulmonary Disease (COPD) exacerbations can improve recovery and reduce the risk of hospitalization. Digital therapeutics are digital interventions, based on best evidence, designed to provide home-based, patient-centered and pervasive self-management support to patients. Digital therapeutics can be effectively used to offer personalized and explainable self-management and behaviour modification resources to patients to reduce the burden of COPD, especially the prevention of acute COPD exacerbations. The functionalities of COPD specific digital therapeutics for self-management need to be grounded in clinical evidence and behavioral theories, in keeping with the self-management needs of COPD patients and their care providers. In this paper, we report the functionalities of a COPD digital therapeutic mobile application based on a needs analysis qualitative study involving both COPD patients and physicians, and, based on the study's finding, we present a knowledge-driven digital therapeutic for COPD self-management.

Temporal Phenotyping for End-Stage Renal Disease Using Longitudinal Electronic Health Records

End Stage Renal Disease (ESRD) is a highly heterogeneous disease with significant differences in prevalence, mortality, complications, and treatment modalities across age, sex, race, and ethnicity. An improved knowledge of disease characteristics results from the use of a data-driven phenotypic classification strategy to identify patients of different subtypes and expose the clinical traits of different subtypes. This study used topic models and process mining techniques to perform subtyping of ESRD patients on hemodialysis based on real-world longitudinal electronic health record data. The mined subtypes are interpretable and clinically significant, and they can reflect differences in the progression of the disease state and clinical outcomes.

An Exploration of the Certified Health Informatician Australasia (CHIA) Participants

The Certified Health Informatician Australasian (CHIA) is an assessment of a candidate's capabilities measured using a core set of health informatics competencies. The aim of this paper is to describe the outcomes of the first eight years since the program's launch. This paper contributes to the competency framework and certification discourse, and knowledge of the increasing importance and recognition of health informaticians through certification. An analysis of results and possible contributing factors is discussed.

Vision-Based Assistance for Vocal Fold Identification in Laryngoscopy with Knowledge Distillation

Laryngoscopy images play a vital role in merging computer vision and otorhinolaryngology research. However, limited studies offer laryngeal datasets for comparative evaluation. Hence, this study introduces a novel dataset focusing on vocal fold images. Additionally, we propose a lightweight network utilizing knowledge distillation, with our student model achieving around 98.4% accuracy-comparable to the original EfficientNetB1 while reducing model weights by up to 88%. We also present an AI-assisted smartphone solution, enabling a portable and intelligent laryngoscopy system that aids laryngoscopists in efficiently targeting vocal fold areas for observation and diagnosis. To sum up, our contribution includes a laryngeal image dataset and a compressed version of the efficient model, suitable for handheld laryngoscopy devices.

Graph Representation Learning-Based Fixed-Length Clinical Feature Vector Generation from Heterogeneous Medical Records

Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data manipulation-thus, manual feature design is labor-intensive. In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which utilizes unsupervised feature extraction based on graph embedding. Unsupervised learning is performed on a heterogeneous graph using Graph2Vec, and the inclusion of clinically useful data in the obtained embedding representation is evaluated by predicting readmission within 30 days of discharge based on it. The embedded representations are observed to improve predictive performance significantly as the information contained in the graph increases, indicating the suitability of the proposed method for feature design corresponding to clinical information.

Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.

User Centered Rare Disease Clinical Trial Knowledge Graph (RCTKG)

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.

Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE)

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.