Plague has been confirmed as an extremely horrific international quarantine infectious disease attributed to Yersinia pestis. It has an extraordinarily high lethal rate that poses a serious hazard to human and animal lives. With the deepening of research, there has been a considerable amount of literature related to the plague that has never been systematically integrated. Indeed, it makes researchers time-consuming and laborious when they conduct some investigation. Accordingly, integrating and excavating plague-related knowledge from considerable literature takes on a critical significance. Moreover, a comprehensive plague knowledge base should be urgently built. To solve the above issues, the plague knowledge base is built for the first time. A database is built from the literature mining based on knowledge graph, which is capable of storing, retrieving, managing and accessing data. First, 5388 plague-related abstracts that were obtained automatically from PubMed are integrated, and plague entity dictionary and ontology knowledge base are constructed by using text mining technology. Second, the scattered plague-related knowledge is correlated through knowledge graph technology. A multifactor correlation knowledge graph centered on plague is formed, which contains 9633 nodes of 33 types (e.g. disease, gene, protein, species, symptom, treatment and geographic location), as well as 9466 association relations (e.g. disease-gene, gene-protein and disease-species). The Neo4j graph database is adopted to store and manage the relational data in the form of triple. Lastly, a plague knowledge base is built, which can successfully manage and visualize a large amount of structured plague-related data. This knowledge base almost provides an integrated and comprehensive plague-related knowledge. It should not only help researchers to better understand the complex pathogenesis and potential therapeutic approaches of plague but also take on a key significance to reference for exploring potential action mechanisms of corresponding drug candidates and the development of vaccine in the future. Furthermore, it is of great significance to promote the field of plague research. Researchers are enabled to acquire data more easily for more effective research. Database URL: http://39.104.28.169:18095/.

The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network.

We investigate how knowledge similarity between two individuals is systematically related to the likelihood that a serendipitous encounter results in knowledge production. We conduct a field experiment at a medical research symposium, where we exogenously varied opportunities for face-to-face encounters among 15,817 scientist-pairs. Our data include direct observations of interaction patterns collected using sociometric badges, and detailed, longitudinal data of the scientists' postsymposium publication records over 6 years. We find that interacting scientists acquire more knowledge and coauthor 1.2 more papers when they share some overlapping interests, but cite each other's work between three and seven times less when they are from the same field. Our findings reveal both collaborative and competitive effects of knowledge similarity on knowledge production outcomes.

Assessing dementia knowledge is critical for developing and improving effective interventions. There are many different tools to assess dementia knowledge, but only one has been validated in German so far.

Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem. Knowledge graph embedding (KGE), which aims to represent entities and relations in low-dimensional continuous vector spaces, has been proved to be a promising approach for KG completion. Traditional KGE methods only concentrate on structured triples, while paying less attention to the type information of entities. In fact, incorporating entity types into embedding learning could further improve the performance of KG completion. To this end, we propose a universal Type-augmented Knowledge graph Embedding framework (TaKE) which could utilize type features to enhance any traditional KGE models. TaKE automatically captures type features under no explicit type information supervision. And by learning different type representations of each entity, TaKE could distinguish the diversity of types specific to distinct relations. We also design a new type-constrained negative sampling strategy to construct more effective negative samples for the training process. Extensive experiments on four datasets from three real-world KGs (Freebase, WordNet and YAGO) demonstrate the merits of our proposed framework. In particular, combining TaKE with the recent tensor factorization KGE model SimplE can achieve state-of-the-art performance on the KG completion task.

Measuring dementia knowledge can be a valuable tool for assessing the effectiveness of dementia awareness activities, identifying the potential benefits of dementia training programs, and breaking down common myths and stereotypes about dementia.

The API and associated software is open source and currently available for access at https://github.com/NCATS-Tangerine/translator-knowledge-beacon.

Anthrax is a zoonotic infectious disease caused by Bacillus anthracis (anthrax bacterium) that affects not only domestic and wild animals worldwide but also human health. As the study develops in-depth, a large quantity of related biomedical publications emerge. Acquiring knowledge from the literature is essential for gaining insight into anthrax etiology, diagnosis, treatment and research. In this study, we used a set of text mining tools to identify nearly 14 000 entities of 29 categories, such as genes, diseases, chemicals, species, vaccines and proteins, from nearly 8000 anthrax biomedical literature and extracted 281 categories of association relationships among the entities. We curated Anthrax-related Entities Dictionary and Anthrax Ontology. We formed Anthrax Knowledge Graph (AnthraxKG) containing more than 6000 nodes, 6000 edges and 32 000 properties. An interactive visualized Anthrax Knowledge Portal(AnthraxKP) was also developed based on AnthraxKG by using Web technology. AnthraxKP in this study provides rich and authentic relevant knowledge in many forms, which can help researchers carry out research more efficiently. Database URL: AnthraxKP is permitted users to query and download data at http://139.224.212.120:18095/.

Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.

Knowledge sharing between individuals is a key process for knowledge-intensive organizations to create value and gain a competitive edge. An individual is in the center of a complex set of factors, which are conducive to the knowledge-sharing process. The purpose of this empirical study is to explain the interaction mechanisms between personality and knowledge-sharing behavior and to examine the mediating effects of willingness to share knowledge and subjective norm. The theory of planned behavior, the social exchange theory, and the big five personality traits theory are combined to explain tacit knowledge-sharing behavior. A survey strategy and purposive sampling was applied, and the analysis was conducted on a sample of 288 employees from Croatia working on knowledge-intensive tasks for which high levels of tacit knowledge sharing are characteristic. A standard online questionnaire consisted of items evaluated on a 7-point Likert-scale, ranging from strongly agree (7) to strongly disagree (1). In the structural model, relationships between altruism, willingness, subjective norm, and tacit knowledge sharing were tested. Confirmatory factor analysis with maximum likelihood estimation was performed by using SEM software AMOS version 23. The findings of the study suggest that altruism has a direct impact on tacit knowledge sharing, reaffirming a relationship with knowledge sharing but distinguishing between sharing of different types of knowledge, assessing tacit knowledge sharing as a construct separate from general knowledge sharing. Our findings suggest that willingness to share is a predictive factor of knowledge sharing behavior between employees, having both direct impact on tacit knowledge sharing and being a mediator between the trait of altruism and tacit knowledge sharing. The mediation test also indicates that altruism has an indirect influence on tacit knowledge sharing when subjective norm was a mediator. The findings suggest that personality traits relying on social capital, such as altruism, have more influence on tacit knowledge sharing compared to personality traits that have accentuated intrinsic components. The study contributes to the better understanding of factors stimulating knowledge-sharing behaviors and provides recommendations based on empirical evidence, which may later be applied in the development of knowledge-sharing leadership styles, employee hiring, and auxiliary initiatives.

In everyday life and in education, we continuously build and structure our knowledge. Successful knowledge construction is suggested to happen through reactivation of previously learned information during new learning. This reactivation is presumed to lead to integration of old and new memories and strengthen long-term retention. Additionally, congruency with prior knowledge is shown to enhance subsequent memory. However, it is unknown how subjective reactivation and congruency jointly influence learning in an educational context. In two experiments, we investigated this question using an AB-AC inference paradigm where students were asked to first study an AB (word-picture) and then an AC-association (word-description). BC-associations were either congruent or incongruent and were linked by a common, unknown word (A). During AC-learning, participants were instructed to actively reactivate B (the picture) and report their subjective reactivation strength. Participants were first-year university students studying either psychology or family studies and the stimuli consisted of new information from their curricula. We expected that both reactivation and congruency would enhance subsequent associative memory for the inferred BC-association. This was assessed by cueing participants with C (the description) and asking to freely describe the associated picture. Results show a significant enhancement of both B-reactivation and congruency on associative memory scores in both experiments. Additionally, subjective meta-memory measures exhibited the same effect. These outcomes, showing beneficial effects of both reactivation and congruency on memory formation, can be of interest to educational practice, where effectively building knowledge through reactivation is imperative for success.

Phenotypic classification is problematic because small samples are ubiquitous; and, for these, use of prior knowledge is critical. If knowledge concerning the feature-label distribution - for instance, genetic pathways - is available, then it can be used in learning. Optimal Bayesian classification provides optimal classification under model uncertainty. It differs from classical Bayesian methods in which a classification model is assumed and prior distributions are placed on model parameters. With optimal Bayesian classification, uncertainty is treated directly on the feature-label distribution, which assures full utilization of prior knowledge and is guaranteed to outperform classical methods.

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept "knowledge unit" and "knowmetrics" for measuring knowledge. However, the definition on "computable knowledge object" remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.

Dementia is a neurodegenerative disease with no cure that can begin up to 20 years before its diagnosis. A key priority in patients with dementia is the identification of early modifiable factors that can slow the progression of the disease. Community pharmacies are suitable points for cognitive-impairment screening because of their proximity to patients. Therefore, the continuous training of professionals working in pharmacies directly impacts the public health of the population. The main purpose of this study was to assess community pharmacists' knowledge of dementia-related factors. Thus, we conducted a cross-sectional study of 361 pharmacists via an online questionnaire that quizzed their knowledge of a list of dementia-related factors, which we later arranged into the A-to-Z Dementia Knowledge List. We found that younger participants had a better knowledge of risk factors associated with dementia. The risk factors most often identified were a family history of dementia followed by social isolation. More than 40% of the respondents did not identify herpes labialis, sleep more than 9 h per day, and poor hearing as risk factors. A higher percentage of respondents were better able to identify protective factors than risk factors. The least known protective factors were internet use, avoidance of pollution, and the use of anti-inflammatory drugs. Pharmacists' knowledge of dementia-related factors should be renewed with the aim of enhancing their unique placement to easily implement cognitive-impairment screening.

Eating disorders are serious psychological disorders with long term health impacts. Athletic populations, tend to have higher incidences of eating disorders compared to the general population. Yet there is little known about athletes' eating disorder knowledge and how it relates to their confidence in their knowledge. Therefore, the purpose of our study was to evaluate collegiate female athletes' eating disorder (ED) knowledge and confidence in their knowledge. 51 participants were recruited from a National Association of Intercollegiate Athletics (NAIA) university in the mid-west and asked to complete a 30-question exam assessing one's knowledge of five different categories related to eating disorders. Confidence in the correctness of answers was assessed with a 5-point Likert-scale (1 = very unconfident, 5 = very confident). A one-way ANOVA was used to determine differences between scores on different categories and overall scores. A simple regression analysis was used to determine if confidence or age was predictive in knowledge scores.

Kawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects of Kawasaki Disease. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. In this paper, we present an approach for the construction of Knowledge Graphs of Kawasaki Disease. It integrates a wide range of knowledge resources related to Kawasaki Disease, including clinical guidelines, clinical trials, drug knowledge bases, medical literature, and others. It provides a basic integration foundation of knowledge and data concerning Kawasaki Disease for clinical study. In this paper, we will show that this disease-specific Knowledge Graphs are useful for exploring various aspects of Kawasaki Disease.

In recent years, the act of nonconsensual condom removal, termed stealthing, has become commonly discussed on social and print media; yet, little to no evidence exists on the current knowledge and perception of stealthing among young adults. As such, we assessed what college students know and feel regarding stealthing. We employed an exploratory mixed-method analysis where focus groups were followed by a quantitative survey. A qualitative assessment was conducted using grounded theory analyses and questions for a quantitative survey were developed based on emergent themes from focus groups. Quantitative data was analyzed using descriptive and bivariate analyses with alpha less than 0.05 to denote significance. Though limited knowledge exists, participants felt that stealthing was a violation of their privacy, trust, sexual consent, and their ability to make a health decision, and should be considered an assault. Participants noted stealthing may have become acceptable due to its popularity in social media and young adult culture, especially porn. We also found sex differences in the perception of stealthing being considered a sexual assault with lower rates among males as compared to females. Our results demonstrate that there is a need for health educators to assess the prevalence of such a behavior among college students and policy makers to assess the legal implications of nonconsensual condom removal.

To understand how video telephone technology could support consultations between pathologists and surgeons, this study looked at what constitutes 'work' in clinical consultations. Using several methods (participant observation, video and interviews), we found pathologists and surgeons both share and do not share similar understandings of what a consultation is, what one should achieve in a consultation, and what in fact constitutes a 'successful' consultation. Furthermore, the same objects of consultation (the products of 'offstage' work) can be used and defined quite differently depending on how a consultation is framed. Differences and disjunctions like these have to be better understood if computer-supported cooperative healthcare work (CSCHW) applications are to be adopted and accepted.

The theory of probability is widely used in biomedical research for data analysis and modelling. In previous work the probabilities of the research hypotheses have been recorded as experimental metadata. The ontology HELO is designed to support probabilistic reasoning, and provides semantic descriptors for reporting on research that involves operations with probabilities. HELO explicitly links research statements such as hypotheses, models, laws, conclusions, etc. to the associated probabilities of these statements being true. HELO enables the explicit semantic representation and accurate recording of probabilities in hypotheses, as well as the inference methods used to generate and update those hypotheses. We demonstrate the utility of HELO on three worked examples: changes in the probability of the hypothesis that sirtuins regulate human life span; changes in the probability of hypotheses about gene functions in the S. cerevisiae aromatic amino acid pathway; and the use of active learning in drug design (quantitative structure activity relation learning), where a strategy for the selection of compounds with the highest probability of improving on the best known compound was used. HELO is open source and available at https://github.com/larisa-soldatova/HELO.