Knowledge representation

BACKGROUND: Diagnostic reasoning is a complex cognitive process that requires intuitive, heuristic processing from knowledge and experience, as well as deliberate and reflective thinking. Evidence on interventions to improve diagnostic reasoning is inconsistent, in part because different terms and models are used to guide research. PURPOSE: To present a model of the factors of diagnostic reasoning in Advanced Practice Registered Nurses (APRNs), based on a review of the literature supporting the Metacognitive Diagnostic Reasoning (MDR) Model©...

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size...

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures...

A long history of policymaking and regulation constructed for the purpose of ensuring adequate fetal and infant protections has inadvertently sanctioned the widespread exclusion of pregnant and lactating patients from biomedical research, leaving a paucity of high quality data necessary for clinical decision-making. Although well-intended, the regulatory classification of pregnant women as "vulnerable", in conjunction with burdensome enrollment criteria and other factors weighing against broad inclusion, have ultimately placed the health and safety of these women and their babies in jeopardy...

This article aims to clarify citizens' responses to conspiratorial anti-science beliefs (e.g. "The cure for cancer exists but is hidden from the public by commercial interests"). Based on Eurobarometer 95.2 (Spring 2021, 38 countries), we examine how public opposition or support for conspiratorial anti-science beliefs is related to individual- and country-level variables. There were large differences between the countries in their opposition or support. Controlling for artifacts, the individual-level variables showed associations with science-specific variables, for example, knowledge, preferred communication sources, social evaluations of scientists, attitude toward vaccines, and more general political (dis)satisfaction...

When facing an unfamiliar environment, animals need to explore to gain new knowledge about which actions provide reward, but also put the newly acquired knowledge to use as quickly as possible. Optimal reinforcement learning strategies should therefore assess the uncertainties of these action-reward associations and utilise them to inform decision making. We propose a novel model whereby direct and indirect striatal pathways act together to estimate both the mean and variance of reward distributions, and mesolimbic dopaminergic neurons provide transient novelty signals, facilitating effective uncertainty-driven exploration...

Many research works have shown that the traditional alternating direction multiplier methods (ADMMs) can be better understood by continuous-time differential equations (DEs). On the other hand, many unfolded algorithms directly inherit the traditional iterations to build deep networks. Although they achieve superior practical performance and a faster convergence rate than traditional counterparts, there is a lack of clear insight into unfolded network structures. Thus, we attempt to explore the unfolded linearized ADMM (LADMM) from the perspective of DEs, and design more efficient unfolded networks...

The concept of interval-valued intuitionistic fuzzy sets is intellectually stimulating and holds significant utility in the representation and analysis of real-world problems. The development of similarity measures within the class of interval-valued intuitionistic fuzzy sets possesses significant importance across various academic disciplines, particularly in the fields of decision-making and pattern recognition. The utilization of similarity measures is of utmost importance in the decision-making process when implementing interval-valued intuitionistic fuzzy sets...

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities...

The ultrafast nonadiabatic excited state dynamics of (PTZ-N-benzyl-acetylide) ( trans -bis-trimethylphosphine) Pt(II) (acetylide-NDI-bis-methyl) 1 , representative of a series of Pt(II) donor-bridge-acceptor assemblies experimentally studied by the Weinstein group, University of Sheffield, is investigated by means of wavepacket propagations based on the multiconfiguration time-dependent Hartree (MCTDH) method. On the basis of electronic structure data obtained at the time-dependent density functional theory (TD-DFT) level, the subpicosecond decay is simulated by solving an 11 electronic states multimode problem, up to 18 vibrational normal modes, including both spin-orbit coupling (SOC) and vibronic coupling...

OBJECTIVE: Elite athletes are at elevated risk for disordered eating and eating disorders; however, little is known about risk and maintaining factors, or barriers and facilitators of help-seeking in this cohort. This systematic review synthesized qualitative findings regarding possible risk and maintaining factors for disordered eating, as well as barriers to and facilitators of help-seeking in elite athletes. METHOD: We conducted a search for qualitative studies that included experiences with body image concerns or disordered eating in elite athletes...

The current study was motivated by an interest in deepening understanding of Brazilian twin research, which is underrepresented internationally, in an effort to rectify this situation. Our aim was threefold: (1) to carry out a comprehensive investigation of Brazilian research on twins according to the area of knowledge; (2) to evaluate the representation of research in the field of psychology in comparison with other areas; (3) to evaluate characteristics of the research that may have contributed to its exclusion from the comprehensive meta-analysis of 50 years of twin research...

We present a novel approach to representing perceptual and cognitive knowledge, spectral knowledge representation , that is focused on the oscillatory behaviour of the brain. The model is presented in the context of a larger hypothetical cognitive architecture. The model uses literal representations of waves to describe the dynamics of neural assemblies as they process perceived input. We show how the model can be applied to representations of sound, and usefully model music perception, specifically harmonic distance...

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e...

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines...

Chemical-Disease relation (CDR) extraction aims to identify the semantic relations between chemical and disease entities in the unstructured biomedical document, which provides a basis for downstream tasks such as clinical medical diagnosis and drug discovery. Compared with general domain relation extraction, it needs a more effective representation of the whole document due to the specialized nature of texts in the biomedical domain, including the biomedical entity and entity-pair representation. In this paper, we propose a novel Multi-view Merge Representation (MMR) model to thoroughly capture entity and entity-pair representation of the document...

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model...

There is an increasing demand for navigation capability for space vehicles. The exploitation of the so-called Space Service Volume (SSV), and hence the extension of the Global Navigation Satellite System (GNSS) from terrestrial to space users, is currently considered a fundamental step. Knowledge of the constellation antenna pattern, including the side lobe signals, is the main input for assessing the expected GNSS signal availability and navigation performance, especially for high orbits. The best way to define and share this information with the final GNSS user is still an open question...

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system...

This review provides a comprehensive summary of the skin penetration pathways of xenobiotics, including metals, organic pollutants, and nanoparticles (NPs), with a particular focus on the methodologies employed to elucidate these penetration routes. The impacts of the physicochemical properties of exogenous substances and the properties of solvent carriers on the penetration efficiencies were discussed. Furthermore, the review outlines the steady-state and transient models for predicting the skin permeability of xenobiotics, emphasizing the models which enable realistic visualization of pharmaco-kinetic phenomena via detailed geometric representations of the skin microstructure, such as stratum corneum (SC) (bricks and mortar) and skin appendages (hair follicles and sebaceous gland units)...