Multiple Knowledge Research Articles

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

Benefits and challenges of reviewing across knowledge systems: ‘Gourmet omnivore’ pigs foraging in the wild

Abstract Evidence‐based conservation can benefit substantially from multiple knowledge sources and different knowledge systems. While traditional ecological knowledge (TEK) and collaborative research are increasingly acknowledged, detailed cross‐knowledge system reviews are scarce and their methodology underdeveloped. We have two objectives: to prepare such a review and to discuss the benefits and challenges of such reviews. We review pig keeping in forests and marshes, a historically widespread but nowadays almost extinct practice in Europe, but one with high potential for organic farming, conservation and restoration. We focus on what, when and how free‐ranging pigs forage in the wild. We review five knowledge sources: living and archived TEK, pig and wild boar scientific literature, and the authors' observations of foraging. Unexpectedly, given the amount of available information, archived TEK differed considerably from living TEK of svinjars (Serbian: traditional pig keepers), and scientific knowledge on pig and boar foraging from TEK. Svinjars deeply understood the consumption and avoidance behaviour of pigs towards 98 and 56 plant taxa, and 42 and 17 animal taxa, respectively. Our review showed that pigs are gourmet omnivores, optimizing and switching between foraging on earthworms, acorns, grasses and corn. Discrepancies between knowledge sources were rare, for example on the consumption of woody roots, earthworms, mushrooms and snakes/lizards. Sources were also complementary, filling each other's knowledge gaps. Topics where the cross‐knowledge system review was most fruitful were acorn foraging, browsing, earthworm and mushroom consumption. Differences in the contributions of the knowledge sources to the enriched picture resulted from the diverging interests and methodologies of the knowledge generators. We identified and discussed both the benefits (different approaches of knowledge generation; expanded time scales; complementarity; novel cause–effect explanations; identification of knowledge gaps; and biases) and the challenges (how to identify relevant publications and knowledgeable TEK holders; how to collate knowledge and verify its reliability; and how to conduct a culturally respectful synthesis) of cross‐knowledge system reviewing. Synthesis and applications. Cross‐knowledge system reviews help overcome limitations in ecological understanding and may provide a shared understanding among collaborating partners, build trust and foster acceptance of each other's knowledge as legitimate. Read the free Plain Language Summary for this article on the Journal blog.

Energy Justice as Epistemic Justice

ABSTRACT Energy justice is often conceived of as consisting of distributive, procedural, and recognitional justice. This article adds epistemic justice, which engages with the question of how the exchange of knowledge can be shaped fairly. Energy issues ramify across social worlds, connecting to multiple knowledge systems. The conventional elements of energy justice place specific demands on how different knowledge systems must be accommodated. Epistemic work must be done to bridge epistemological differences and pay due respect to different forms of knowledge. Energy justice should not neglect this epistemic work, and it cannot be assumed that it is sufficient to invite stakeholders to the discussion arena. I argue that fair ways of dealing with knowledge and fair opportunities for citizens to contribute knowledge to energy affairs are crucial to achieving energy justice of any general form. Also, this epistemic justice is not to be seen as yet another pillar of energy justice but a concern that runs through all pillars of energy justice.

MuHeQA: Zero-shot question answering over multiple and heterogeneous knowledge bases

There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.

NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research.

NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimer's Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud-based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimer's disease.

Transformative Earth stewardship: Principles for shaping a sustainable future for nature and society

AbstractEarth stewardship is the proactive shaping of pathways of social‐ecological change to enable people and nature to thrive. Its strategy is to identify and shape sustainable pathways of human–nature interaction. I describe three Earth stewardship approaches to addressing global changes. Two of these (resisting unfavorable change; accepting and adapting to change) are well‐accepted approaches to conservation and sustainability. The third (transformative Earth stewardship to escape unsustainable traps) is more challenging but can sometimes overcome seemingly insurmountable barriers. It can be triggered by a change in vision for society, changes in feedbacks such as social norms and incentives, or engagement of influential actors or institutions. The innovation necessary to trigger profound transformation often requires transdisciplinary perspectives that draw on multiple knowledge systems, modes of communication, and sectors of society.

Evaluating public awareness and knowledge of multiple sclerosis in Tobruk, Libya: a comprehensive cross-sectional study

BackgroundMultiple sclerosis (MS) is a chronic and often disabling disease that impacts the central nervous system, leading to significant public health concerns. This study aimed to assess the level of awareness and knowledge of MS among the population of Tobruk, Libya.MethodsA community-based cross-sectional study was conducted in Tobruk, Libya, during August 2023. A total of 1,427 participants were surveyed using the Multiple Sclerosis Knowledge Questionnaire (MSKQ-25). Data were collected via an online Google form and analyzed using RStudio and SPSS. The analysis included descriptive statistics, Chi-square tests, t-tests, Mann-Whitney tests, ANOVA, Kruskal-Wallis tests, and Pearson’s correlation coefficients.ResultsThe study found a low level of MS awareness among participants, with a mean MSKQ-25 score of 8.32 ± 5.44. Significant differences in MS knowledge were observed between individuals with relatives who had MS and those without (p < 0.001), and between medical field students and students from other backgrounds (p < 0.001). No significant association was found between MS knowledge scores and marital status or educational levels. However, significant associations were observed between MS knowledge and age, gender, and financial status (p < 0.05 for all). The majority of participants lacked knowledge about genetics, etiology, and treatment of MS.ConclusionThe findings highlight a critical need for comprehensive public outreach and education programs to improve MS awareness in Tobruk, Libya. Health education initiatives focused on MS are essential for increasing community knowledge, facilitating early detection, and improving disease management. Further research is needed to understand the reasons for misinformation and to develop strategies for increasing awareness across the country.

Decisional needs for older adults, home health care nurses and dental hygienists during team-based oral health assessments in ordinary home settings – a qualitative study

BackgroundParticipation by all actors involved in health planning is a prerequisite for person-centred care and healthy ageing. Understanding the multiple knowledge needs and the values that shape oral health assessments in home settings is important both to enable participation in oral health planning and to contribute to healthy ageing.ObjectiveThe aim of this study was to investigate decisional needs during oral health assessments in ordinary home settings from the perspectives of older adults, home health care nurses and dental hygienists.MethodsData was collected in ordinary home settings through 24 team-based oral assessments and 39 brief, semi-structured interviews including older adults (n = 24), home health care nurses (n = 8) and dental hygienists (n = 7). Data was analysed using content analysis with a deductive approach. The analysis was guided by the Ottawa Decision Support Guide.ResultsThe analysis revealed that all participants considered participation in decision-making important but until now, older adults might not have participated in making decisions regarding oral health issues. The older adults considered participation important because the decisions had a strong impact on their lives, affecting their health. The professionals considered decision-making important for knowing what step to take next and to be able to follow up and evaluate previous goals and treatments. Organizational and personal barriers for shared decision-making among home health care nurses and dental hygienists were identified. Of the 24 older adults, 20 had different oral health conditions that objectively indicated the need for treatment. An initial important decision concerned whether the older adult wanted to make an appointment for dental care, and if so, how. Another decisional conflict concerned whether and how assisted oral care should be carried out.ConclusionIt is important for key participants in ordinary home settings to participate in interprofessional teams in home health care. To further anchor this in theory, conceptual models for professionals from different care organizations (municipal care, dental care) need to be developed that also involve older adults as participants. Future research could bridge theory and practice by including theories of learning while exploring interorganizational oral health planning in home settings.

Effectiveness of a pediatric critical care pilot preceptor program: Improved confidence and competency outcomes among a cohort of professional nurses in Blantyre, Malawi

BackgroundAvailability of high-level pediatric training for nurses in Malawi is limited. To address this gap, a novel pediatric critical care nurse preceptor program was developed and implemented by pediatric nurse specialists. AimEvaluate the effectiveness of a pediatric critical care nurse preceptor program, via change in nurses' knowledge, skills, confidence, and precepting competence. DesignA 12-month pediatric critical care nurse preceptor program with assessments at baseline, end of intensive (3 months), and end of program (6 months). SettingBlantyre, Malawi. ParticipantsNurses with two or more years of pediatric nursing experience (N = 20) nominated by unit managers. MethodsQuantitative data were collected throughout program implementation. Assessments included: (1) multiple choice knowledge test, (2) Objective Structured Clinical Examinations in two areas (vital signs and airway, breathing, circulation, disability, exposure assessments; and blood gas and electrolyte analysis), (3) group simulations (cardiopulmonary resuscitation or respiratory distress), (4) Likert-scale clinical confidence survey, and (5) Likert-scale precepting competence survey. Data were analyzed using repeated measures ANOVA with pairwise comparisons. For Likert-scale surveys, median confidence scores were compared using a Friedman test with post hoc pairwise comparisons using Wilcoxon signed-rank tests. ResultsParticipants demonstrated significant improvement in clinical knowledge (p < .001), vital signs and airway, breathing, circulation, disability, exposure assessment (p = .001), blood gas and electrolyte analysis (p = .001), CPR (p < .001) and respiratory distress (p < .001) simulations, clinical confidence (p = .002), and precepting competence (p = .041). ConclusionThis pediatric critical care nurse preceptor program was effective in improving participants' confidence and competence (knowledge and skills) in pediatric critical care nursing and precepting. Results suggest the program's potential to address the shortage of highly trained pediatric critical care nurses in Malawi. This lays groundwork for refining and expanding preceptorship, ultimately improving pediatric critical care nursing education in resource-limited settings.

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models

BackgroundVaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance.ResultsIn this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, the Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate’s accuracy and 90.0% on top 10 candidate’s accuracy.ConclusionThis study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.

The Impact of Multilingual Education On Developing Communication Skills for University Students Within Foreign Language Learning

Today, multilingualism can be considered as an undeniable reality. The rapid pace of globalisation processes and the fast information spreading compelled society to adapt and keep pace with the new realities. These shifts require significant language proficiency and advanced communication skills. In this sense, the command over multiple languages not only broadens one’s horizons and enhances his and/or her professionalism but also facilitates cross-cultural communication as well as the acquisition of multiple knowledge. The relevance of this study is featured by the dynamic changes in society, highlighting the need of multilingual education for people to fully realise their potential in life. This research investigates the impact of multilingual education on developing communication skills for University students within foreign language learning. It explores the principles of multilingual learning and identifies effective pedagogical methods that enhance language abilities, with a particular emphasis on the role of technology, delving into strategies like gamification and virtual classrooms. Furthermore, this research thoroughly examines the different ways multilingual education affects communication skill development in the study of foreign languages at universities. It aims at identifying progressive teaching methods and strategies that improve the quality of students' language acquisition. It is important to point out that higher education institutions serve as the keystone for multilingual education, providing students not only with linguistic competencies but also with professional knowledge to expand their career prospects. The methodological framework employed in this research provides a comprehensive approach, integrating historical, social, linguodidactic, and pedagogical concepts alongside several scientific methods. The study refers to methods of scientific cognition such as systematic analysis, synthesis, dialectical inquiry, and normative methods to enrich its findings. Moreover, significant advantages of multilingual learning, particularly in enhancing English language proficiency and communication skills, are identified. Also, the study stresses the focal role of modern technologies in multilingual education, highlighting their potential to transform learning experiences and personalise educational approaches. Particularly, the research underscores how multilingual education fosters communication skills, notably in English as a global lingua franca.

GFedKG: GNN-based federated embedding model for knowledge graph completion

Knowledge Graph (KG) completion through reasoning over KGs is one of the most concerned issue in research around knowledge graphs. Current work focuses more on studying individual KG, while a series of related multi-source KGs are more common in real applications. In order to effectively exert their complementary roles in completing each KG under the premise of ensuring data privacy, federated learning becomes the focus of attention. However, existing methods ignore the explicit complementarity among KGs in terms of structural information. And they generally align embeddings of overlapping instances across clients, which constrains flexibility of each client model. Based on this, we propose the first GNN-based federated embedding framework for multiple knowledge graph completion, GFedKG. It focuses on the complementary role among KGs in topology structures and adaptively generates different aggregated neighboring features for entities in distinct clients. Such treatment achieves some consistency among clients while retaining the flexibility of each client model to characterize respective KG. Moreover, entity embeddings that represent the features of entities are better privacy-preserved since neighboring embeddings are transmitted instead of them. And with the aim of realizing more refined and accurate aggregation of neighboring features from multiple clients, we innovatively propose a context-aware graph neural network, along with the context-enhanced evaluation function. Homophily neighbors are also taken into account to cope with the low-homophily aspect of KGs. Extensive experiments demonstrate the advantages of GFedKG.

Assessing nature’s contributions to people in a peri-urban protected area, southeast Brazil: integrating feedback loops and participatory scenario planning

ABSTRACT Urban expansion is a key driver of change that has been affecting nature’s contributions to people (NCP) in peri-urban regions near metropolitan areas. In southeast Brazil, Campinas Environmental Protected Area (EPA) is a peri-urban, sustainable-use protected area located within the metropolis of Campinas. Campinas EPA provides relevant NCP at the local and regional level, including water provision for 1.2 million people, nature-based tourism and food production. We conducted a participatory, qualitative assessment of past and future trends of NCP at Campinas EPA, in collaboration with the management council of the protected area. We collected data through direct observation, semi-structured interviews and scenario workshops. In our research process, we innovate using the NCP framework, a concept acknowledging multiple values and knowledge systems, coupled with ecosystem services, a concept already understood by management councilors. To assess past trends (2000–2020), we built a causal-loop diagram which depicts an amplifying feedback loop that fosters change towards an urban-dominated state. To assess future trends (2020–2040), we facilitated scenario planning workshops to create and contrast a business-as-usual and a desirable future for NCP, illustrated in the form of artistic panels. We identified a local agroecological farm that represents a seed of good anthropocenes – i.e. the farm is an innovative example of what a positive future could look like, thus providing relevant evidence on how to achieve a desirable scenario. Our findings shed light on how urban expansion within peri-urban areas impacts NCP and suggest strategies for positive transformations.

Climate Justice and Climate Adaptation in California: Indigenous Community Climate Adaptation Leadership and Opportunities for Scientific Collaboration

Abstract Climate and weather-related disasters in California illustrate the need for immediate climate change action—both mitigation to reduce impacts and adaptation to protect our communities, relatives, and the ecosystems we depend upon. Indigenous frontline communities face even greater threats from climate impacts due to historical and political legacies of environmental injustice. Climate change adaptation actions have proven challenging to implement as communities struggle to access necessary climate data at appropriate scales, identify effective strategies that address community priorities, and obtain resources to act at a whole-community level. In this paper, we present three examples of Indigenous communities in California that have used a climate justice approach to climate change adaptation. These communities are drawing upon community knowledge and expertise to address the challenges of adaptation planning and taking actions that center community priorities. The three cases address emergency preparation and response, cultural burning and fire management, and community organizing and social cohesion. Across these spheres, they illustrate the ways in which a community-based and climate justice-focused approach to adaptation can be effective in addressing current threats while also addressing the legacy of imposed, socially constructed vulnerability and environmental injustices. Because we recognize the need for multiple knowledges and skills in adaptation actions, we include recommendations that have emerged based on what has been learned through these long-standing and engaged participatory research collaborations for climate scientists who wish to contribute to climate justice-focused adaptation efforts by using scientific data to support—not supplant—community efforts, target funding toward genuine community engagement and adaptation actions, and become aware of the historical and political legacies that created the climate vulnerabilities and injustices evident today.

Towards assessing the quality of knowledge graphs via differential testing

Knowledge graphs (KG) can aggregate data and make information resources easier to calculate and understand. With tremendous advancements in knowledge graphs, they have been incorporated into plenty of software systems to assist various tasks. However, while KGs determine the performance of downstream software systems, their quality is often measured by the accuracy of test data. Considering the limitation of accessible high-quality test data, an automated quality assessment technique could fundamentally improve the testing efficiency of KG-driven software systems and save plenty of manual labeling resources.In this paper, we propose an automated approach to quantify the quality of KGs via differential testing. It first constructs multiple Knowledge Graph Embedding Models (KGEM) and conducts head prediction tasks on models. Then, it can produce a differential score that reflects the quality of KGs by comparing the proximity of output results. To validate the effectiveness of this approach, we experiment with four open-sourced knowledge graphs. The experiment results show that our approach is capable of accurately evaluating the quality of KGs and producing reliable results on different datasets. Moreover, we compared our method with existing methods and achieved certain advantages. The potential usefulness of our approach sheds light on the development of various KG-driven software systems.

Cree-driven community-partnered research on coastal ecosystem change in subarctic Canada: a multiple knowledge approach

Indigenous-driven and community-partnered research projects seeking to develop salient, legitimate, and credible knowledge bases for environmental decision-making require a multiple knowledge systems approach. When involving partners in addition to communities, diverging perspectives and priorities may arise, making the pathways to engaging in principled research while generating actionable knowledge unclear to disciplinarily-trained natural science researchers. Here, we share insights from the Eeyou Coastal Habitat Comprehensive Research Project (CHCRP), an interdisciplinary, Cree-driven community-academic partnership. This project brought together Cree community members, regional organizations, industry (Hydro-Québec), and academics from seven universities across Canada to address the unprecedented loss of seagrass Zostera marina (eelgrass), the concurrent decline in migratory Canada geese and its impact on fall goose harvest activities in Eeyou Istchee. After describing the history and context of the project, we discuss the challenges, complexities, and benefits of the collaborative approach balancing saliency, legitimacy and credibility of the knowledge produced. We suggest the paper may be of use to researchers and partners seeking to engage in principled and actionable research related to environmental change including impacts of past development.

We urgently need multiple knowledges to achieve the Sustainable Development Goals

We urgently need multiple knowledges to achieve the Sustainable Development Goals

The Challenging Approach to Multiple Myeloma: From Disease Diagnosis and Monitoring to Complications Management.

The outcome of multiple myeloma (MM) has significantly improved in the last few decades due to several factors such as new biological discoveries allowing to better stratify disease risk, development of more effective therapies and better management of side effects related to them. However, handling all these aspects requires an interdisciplinary approach involving multiple knowledge and collaboration of different specialists. The hematologist, faced with a patient with MM, must not only choose a treatment according to patient and disease characteristics but must also know when therapy needs to be started and how to monitor it during and after treatment. Moreover, he must deal not only with organ issues related to MM such as bone disease, renal failure or neurological disease but also with adverse events, often very serious, related to novel therapies, particularly new generation immunotherapies such as CAR T cell therapy and bispecific antibodies. In this review, we provide an overview on the newer MM diagnostic and monitoring strategies and on the main side effects of MM therapies, focusing on adverse events occurring during treatment with CAR T cells and bispecific antibodies.

Virtual reality simulation to enhance advanced trauma life support trainings – a randomized controlled trial

BackgroundAdvanced Trauma Life Support (ATLS) is the gold standard of initial assessment of trauma patients and therefore a widely used training program for medical professionals. Practical application of the knowledge taught can be challenging for medical students and inexperienced clinicians. Simulation-based training, including virtual reality (VR), has proven to be a valuable adjunct to real-world experiences in trauma education. Previous studies have demonstrated the effectiveness of VR simulations for surgical and technical skills training. However, there is limited evidence on VR simulation training specifically for trauma education, particularly within the ATLS curriculum. The purpose of this pilot study is to evaluate the feasibility, effectiveness, and acceptance of using a fully immersive VR trauma simulation to prepare medical students for the ATLS course.MethodsThis was a prospective randomised controlled pilot study on a convenience sample of advanced medical students (n = 56; intervention group with adjunct training using a commercially available semi-automated trauma VR simulation, n = 28, vs control group, n = 28) taking part in the ATLS course of the Military Physician Officer School.Feasibility was assessed by evaluating factors related to technical factors of the VR training (e.g. rate of interruptions and premature termination). Objective and subjective effectiveness was assessed using confidence ratings at four pre-specified points in the curriculum, validated surveys, clinical scenario scores, multiple choice knowledge tests, and ATLS final clinical scenario and course pass rates. Acceptance was measured using validated instruments to assess variables of media use (Technology acceptance, usability, presence and immersion, workload, and user satisfaction).ResultsThe feasibility assessment demonstrated that only one premature termination occurred and that all remaining participants in the intervention group correctly stabilised the patient. No significant differences between the two groups in terms of objective effectiveness were observed (p = 0.832 and p = 0.237 for the pretest and final knowledge test, respectively; p = 0.485 for the pass rates for the final clinical scenario on the first attempt; all participants passed the ATLS course). In terms of subjective effectiveness, the authors found significantly improved confidence post-VR intervention (p < .001) in providing emergency care using the ATLS principles. Perceived usefulness in the TEI was stated with a mean of 4 (SD 0.8; range 0–5). Overall acceptance and usability of the VR simulation was rated as positive (System Usability Scale total score mean 79.4 (SD 11.3, range 0–100).ConclusionsThe findings of this prospective pilot study indicate the potential of using VR trauma simulations as a feasible and acceptable supplementary tool for the ATLS training course. Where objective effectiveness regarding test and scenario scores remained unchanged, subjective effectiveness demonstrated improvement.Future research should focus on identifying specific scenarios and domains where VR can outperform or enhance traditional learning methods in trauma simulation.

SAILoR: Structure-Aware Inference of Logic Rules.

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.