The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text-mining

Motivation: Epilepsy is a multi-faceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment, and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain.Results: The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies, and scientific literature. This ontology consists of 1,879 classes and 28,151 axioms (2,171 declaration axioms, 2,219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes.

Microfinancial Collaborative Economies Business Models and Strategies for Quality of Life Improvement

The present chapter analyzes two cases of a joint venture stage to determine the successes and failures undertaken by the PROMUSAG and Uber as a model and strategies of collaborative economies to improve the quality of life. First, it is analyzed PROMUSAG as a program to finance women entrepreneurship aimed to improve the quality of life and the second case aims to analyze the different strategies taken by Uber to join the global market successfully, positioning itself in different countries. The analysis concludes that the empirical knowledge of entrepreneurs, in this case were not sufficient to direct the business to success, and that the lack of structured knowledge and adequate scientific support for this project strongly directed towards the non-permanence on the market. Taking terms as work global, it is considered Uber as a technology-based company and sees it from an overall, same strategy refers to a strategy that follows the company having a worldwide standardized product, another issue that would revise the importance of the theory of institutions.

Pipeline for retrieval of COVID-19 immune signatures

Objective: The accelerating pace of biomedical publication has made retrieving papers and extracting specific comprehensive scientific information a key challenge. A timely example of such a challenge is to retrieve the subset of papers that report on immune signatures (coherent sets of biomarkers) to understand the immune response mechanisms which drive differential SARS-CoV-2 infection outcomes. A systematic and scalable approach is needed to identify and extract COVID-19 immune signatures in a structured and machine-readable format. Materials and Methods: We used SPECTER embeddings with SVM classifiers to automatically identify papers containing immune signatures. A generic web platform was used to manually screen papers and allow anonymous submission. Results: We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. This demonstrates the efficacy of using a SVM classifier with document embeddings of the abstract and title, to retrieve papers with scientifically salient information, even when that information is rarely present in the abstract. Additionally, classification based on the embeddings identified the type of immune signature (e.g., gene expression vs. other types of profiling) with a positive predictive value of 74%. Conclusions: Coupling a classifier based on document embeddings with direct author engagement offers a promising pathway to build a semi-structured representation of scientifically relevant information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.

Students' Processing of Differently Structured Text Materials Focused on Agricultural Waste Disposal Using Eye-tracking

This study was focused on agricultural waste disposal (AWD) textual materials. Two educational texts are compared: designed texts traditionally with no purposeful design and structured knowledge texts, including the textual form of knowledge units. Eye-tracking technology is employed for retrieving the values of critical indicators specifying the way of reading the texts. We analysed users' visual attention and looking behaviour during the reading process. Thirty-three students worked with 45 pieces of educational texts accompanied by a didactic test. Statistical analyses show statistically significant differences neither in any indicator within studying the texts nor in the users' success rate in the didactic test. The users can work with the knowledge structured texts equivalently with the designed texts in the traditional way. The positive effect for AWD is that users can process knowledge structured texts with better results.

NEW VISION OF CONCEPT EUROPE (A LINGUISTIC PERSPECTIVE)

This article presents a study of the concept EUROPE, done in the area of cognitive linguistics. The concept EUROPE is considered a conceptual quantum of structured knowledge, possessing of different meanings. The article analyzes the concept EUROPE from a linguistic point of view, which allowed to reveal a number of specific features of its semantics, to get a more comprehensive and diverse view of it by constructing cognitive schemes implementing concept EUROPE, as well as to identify basic metaphorical models. Involvement of methods of conceptual analysis allows to present the analised concept in the form of a certain conceptual model, a special way organized conceptual scheme. With the help of the conceptual model of the Subject frame the cognitive dynamics of the concept development in the European integration discourse is revealed. Political metaphor is one of the most common and effective policy tools. The research material is characterized by the use of metaphors belonging to such basic types, which are related to the reference spheres as the sociomorphic sphere, the anthropomorphic sphere, the sphere of artifacts and the sphere of nature. Conceptual metaphors of European integration discourse use in their codes the conceptual fields “space”, “travel”, “movement”, “construction”, “work results”, “family relations”, “nature”, “sports”, “art”, etc.

Neural mechanisms of credit assignment for inferred relationships in a structured world

Animals have been proposed to abstract compact representations of a task's structure that could, in principle, support accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. Here, we develop a novel hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, mPFC and lateral orbital frontal cortex track the inferred current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance both of tracking the current position in an abstracted task space and efficient, generalizable representations in prefrontal cortex for supporting flexible learning and inference in structured environments.

Chapter 4. Graph Reasoning Networks and Applications

Despite the significant success in various domains, the data-driven deep neural networks compromise the feature interpretability, lack the global reasoning capability, and can’t incorporate external information crucial for complicated real-world tasks. Since the structured knowledge can provide rich cues to record human observations and commonsense, it is thus desirable to bridge symbolic semantics with learned local feature representations. In this chapter, we review works that incorporate different domain knowledge into the intermediate feature representation.These methods firstly construct a domain-specific graph that represents related human knowledge. Then, they characterize node representations with neural network features and perform graph convolution to enhance these symbolic nodes via the graph neural network(GNN).Lastly, they map the enhanced node feature back into the neural network for further propagation or prediction. Through integrating knowledge graphs into neural networks, one can collaborate feature learning and graph reasoning with the same supervised loss function and achieve a more effective and interpretable way to introduce structure constraints.

To Assess the Knowledge Regarding Glaucoma among General Population

Background: The level of knowledge of glaucoma and their possible determinants in a group of people diagnosed with glaucoma and in a population based group without glaucoma. Studies performed on the prevalence of glaucoma have reported a high proportion of undiagnosed patients. Late diagnosis is related to increased risk of glaucoma associated with visual impairment and disability. Lack of awareness and non-availability of appropriate screening procedures are among the major reasons for non-diagnosis or late diagnosis of glaucoma. The present study has been undertaken to evaluate the level of awareness about glaucoma among the general population. Objective: 1. To assess the knowledge regarding glaucoma among general population. 2. To find an association between the level of knowledge with selected socio demographic variables. Materials and Methods: The study was conducted in selected hospital. Descriptive research approach was used in this study. Hundred people in the general population were selected for the study. Structured knowledge questionnaire was used to collect the data. Results: The show that 1 (1%) had poor level of knowledge, 27(27%) were having an average level of knowledge. Fifty seven percent (57%) had a good level of knowledge, fifth teen present 15 (15%) had very good knowledge. None exhibited excellent level of knowledge. The minimum score was 3 and the maximum score was 12, with the mean score for the test being 7.61 ±1.814 and mean percentage of knowledge was 50.73%.

“A CORRELATIONAL STUDY TO ASSESS THE KNOWLEDGE AND PRACTICE OF DIET ON PREMENSTRUAL SYNDROME AND OCCURRENCE OF PREMENSTRUAL SYNDROME AMONG ADOLESCENT GIRLS IN SELECTED COLLEGE”

Introduction: Premenstrual syndrome(PMS) is a group of symptoms that occur in women typically between ovulation and menstruation. The aim of the study was to evaluate the correlation between the knowledge and practice of diet on PMS and occurrence of PMS among adolescent girls. The objectives of the study were to assess the knowledge on diet of PMS among adolescent girls, to assess the practice on diet of PMS among adolescent girls, and to identify the occurrence of PMSamong adolescent girls, to correlate the practice on diet of PMS and occurrence of PMS among adolescent girls, to associate the knowledge scores on diet of PMS with selected socio - demographic variables. Methodology: The study was undertaken with 60 samples. Purposive sampling technique was used. The research design was correlation prospective design. Structured knowledge questionnaire and checklist were used for collecting the data. The data was analysed by using descriptive and inferential statistics. The study ndings show Result: ed that, among 60 samples , 37(62%) has poor knowledge,20(33%) have good knowledge and 3 (5%) has very good knowledge .At 0.05 level of signicance, the hypothesis (H ) was rejected 1 and(H )was accepted Hence it can be concluded that there is statistically signicant difference in the knowledge level of the adolescent girls 2 regarding the knowledge on practice of diet on PMS. The study outcome revealed that Conclusion: the practice of diet on PMS was moderately positive correlated with occurrence of PMS among adolescent girls

Interactive Development of Adaptive Learning and Memory

Across development, interactions between value-based learning and memory processes promote the formation of mental models that enable flexible goal pursuit. Value cues in the environment signal information that may be useful to prioritize in memory; these prioritized memories in turn form the foundation of structured knowledge representations that guide subsequent learning. Critically, neural and cognitive component processes of learning and memory undergo marked shifts from infancy to adulthood, leading to developmental change in the construction of mental models and how they are used to guide goal-directed behavior. This review explores how changes in reciprocal interactions between value-based learning and memory influence adaptive behavior across development and highlights avenues for future research.