Levels Of Ontology Research Articles

How Do You Become Resilient? A Critical Realist Explanation of the Youth Resilience Process

AbstractAdversities serve as risks, but also opportunities to acquire capacities to adjust positively in future stressors. There is now considerable agreement that resilience should be viewed as a process. However, a key question remains: Why do some individuals exhibit resilience while others do not? The present study aimed to provide a detailed description of the youth resilience process and theorized on the specific mechanisms that support positive adjustment following adversities in early life. In-depth interviews were conducted with a purposive sample of 34 young adults with adverse childhood experiences; analysis followed a paradigm of critical realism. Results were organized in three levels of realist ontology to provide hierarchical and substantive support of findings and theorizations. We propose the Youth Resilience Process Model (Y-RPM), which integrates and builds on existing theories and concepts to explain the mechanisms and different pathways of internal processes that foster resilience among youths.

Онтологизация познания: уровни, направления и средства реализации

The article investigates the problem of ontologization of cognition, i.e. increasing the level of conformity between the knowledge system and being. Various levels of ontologization of knowledge are examined depending on the nature of the reflection of reality in the system of knowledge. Four levels of ontology are distinguished: the first level corresponds to the injective and bijective mappings of reality onto the knowledge system; the second level involves surjective mapping; the third level encompasses knowledge formed through isomorphic mapping of relationships between elements from reality onto the knowledge system; the fourth level involves knowledge formed through isomorphic mapping of relationships between elements within the knowledge system. Prospects for the ontologization of cognition are related to the expansion of metaphysical and consequent knowledge, the elevation to the fourth level of knowledge domains that lack ontologicality, and the expansion of knowledge linking secondary laws and properties of objects to patterns. The main tool for ontologizing cognition should be a general theory of systems, extended with a metaphysical component and supplemented with extensive collections of patterns, primitives, secondary properties and laws. The author is working on such a general systems theory, referred to as the “empirico-metaphysical general theory of systems”. In conclusion, development of an empirico-metaphysical theory of systems promises to provide a robust framework for addressing the challenges of cognition representation, synthesis, and application across diverse domains. As researchers continue to delve into the nuances of ontological alignment, the proposed framework offers a promising avenue for fostering interdisciplinary collaboration and advancing our understanding of the fundamental nature of cognition.

ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction.

Recent developments in molecular networking have expanded our ability to characterize the metabolome of diverse samples that contain a significant proportion of ion features with no mass spectral match to known compounds. Manual and tool-assisted natural annotation propagation is readily used to classify molecular networks; however, currently no annotation propagation tools leverage consensus confidence strategies enabled by hierarchical chemical ontologies or enable the use of new in silico tools without significant modification. Herein we present ConCISE (Consensus Classifications of In Silico Elucidations) which is the first tool to fuse molecular networking, spectral library matching and in silico class predictions to establish accurate putative classifications for entire subnetworks. By limiting annotation propagation to only structural classes which are identical for the majority of ion features within a subnetwork, ConCISE maintains a true positive rate greater than 95% across all levels of the ChemOnt hierarchical ontology used by the ClassyFire annotation software (superclass, class, subclass). The ConCISE framework expanded the proportion of reliable and consistent ion feature annotation up to 76%, allowing for improved assessment of the chemo-diversity of dissolved organic matter pools from three complex marine metabolomics datasets comprising dominant reef primary producers, five species of the diatom genus Pseudo-nitzchia, and stromatolite sediment samples.

Identification of Putative Candidate Genes from Galphimia spp. Encoding Enzymes of the Galphimines Triterpenoids Synthesis Pathway with Anxiolytic and Sedative Effects.

Galphimia spp. is popularly used in Mexican traditional medicine. Some populations of Galphimia exert anxiolytic and sedative effects due to the presence of the modified triterpenoids galphimines. However, the galphimine synthesis pathway has not yet been elucidated. Hence, in this study, a comparative transcriptome analysis between two contrasting populations of Galphimia spp., a galphimine-producer, and a non-galphimine-producer, is performed using RNA-Seq in the Illumina Next Seq 550 platform to identify putative candidates genes that encode enzymes of this metabolic pathway. Transcriptome functional annotation was performed using the Blast2GO in levels of gene ontology. For differential expression analysis, edgeR, pheatmap, and Genie3 library were used. To validate transcriptome data, qPCR was conducted. In producer and non-producer plants of both populations of Galphimia spp., most of the transcripts were grouped in the Molecular Function level of gene ontology. A total of 680 differentially expressed transcripts between producer and non-producer plants were detected. In galphimine-producer plants, a larger number of highly expressed transcripts related to acyclic and polycyclic terpene synthesis were identified. As putative candidate genes involved in the galphimine synthesis pathway, P450 family members and enzymes with kinase activity were identified.

DOMAIN ONTOLOGY DEVELOPMENT FOR CONDITION MONITORING SYSTEM OF INDUSTRIAL CONTROL EQUIPMENT AND DEVICES

Context. Modern intelligent systems of failure identification of control equipment and devices in food industry are based on a complexation of approaches implemented on various methods and algorithms. The feature of such systems is that within them operates a large amount of heterogeneous data and knowledge that are difficult to combine. The use of ontologies of different levels in the system development process solves this problem.
 Objective. Domain ontology development for equipment condition monitoring system as a basis for designing intelligent decision support system with ontology knowledge base.
 Methods. There are different ontology development approaches. They may differ in the quantity of levels and types of ontologies or be a combination of subject and problem domains ontologies depending on the complexity of the problem and the chosen ontology development method. This paper represents two levels of the three-level ontology being developed for intelligent condition monitoring system of control equipment and devices. The upper level is represented by top-level ontology Basic Formal Ontology (BFO) which provides systematization of the meta-level, including temporal part. International standards and technical reports such as IEC 62890, ISO 55000, ISA 95, ISA 106, IEC 62264, ISO 10303-242: 2020 are considered in the development process of the second ontology level – Domain ontology.
 Results. The article provides Domain ontology for equipment condition monitoring system in food industry. The developed Domain ontology systematizes, structures engineering knowledge and uses BFO which provides a set of basic elements at the metalevel. They set the values of the following entities: type of production, methods of failure identification, causes, failures, events, equipment, etc. The developed Domain ontology has semantic cross-links. A fragment of the Domain ontology relationships for the “Control equipment” subclass of “Equipment” class is also presented in the paper.
 Conclusions. The developed ontology can be used to analyze the knowledge base on the causes, locations and types of failures and their identification methods. The developed ontology is a basis for application ontology development.

Muslim Educators’ Pedagogies: Tools for Self, Social, and Spiritual Transformation

In this interpretive research study, Claire Alkouatli inquires into the pedagogical activities Sunni Muslim educators employ in sites of Islamic education that are often marginalized by stereotypes, misperceptions, and charges of anachronism and indoctrination. She invited thirty-five Muslim Canadian educators to share their perspectives on their pedagogies around teaching Islam to children and youth. Her thematic analysis of participants’ variegated descriptions coalesced into a three-theme pedagogical typology. Distinct from mainstream secular pedagogies at the levels of ontology, epistemology, and developmental psychology, Islamic pedagogies are situated within a wider conceptual paradigm. Recognizing their qualities of holism and “double cultural relevance,” they are functionally significant in teachers’ repertoires for helping young Muslims think across paradigms and may contribute to both sociocultural continuity and more equal inter-epistemic interaction in heterogeneous societies.

Impact of microbial inoculants combined with humic acid on the fate of estrogens during pig manure composting under low-temperature conditions

To investigate the efficiency of psychrotrophic cellulose-degrading fungal strains (PCDFSs) and estrogen-degrading bacteria (EDBs) combined with humic acid (HA) on estrone (E1) and 17-β-estradiol (E2) degradation, five compost groups (T, HA, EDB, PCDFS, and CK) were prepared and composted for 32 days at 11–14°C. The results indicated that inoculation increased the temperature to 62.2°C and promoted E1 degradation to the lowest level of 100.1 ng/kg, while E2 was undetected from day 16. Metagenomic analysis revealed that inoculation altered the microbial community structure by increasing the abundance of cellulose-degrading fungi, especially Meyerozyma (16.7%) (among PCDFSs), and of estrogen-degrading bacteria, particularly Microbacterium (13.4%) (involved in EDBs). Moreover, inoculation increased the levels (>0.500%) of Gene Ontology (GO) associated with estrogen degradation, like 3-β-hydroxy-delta 5-steroid dehydrogenase and monooxygenase. Redundancy analysis demonstrated that temperature and Microbacterium were positively correlated with estrogen degradation. Structural equation model indicated that temperature and estrogen-degrading bacterial genera exhibited positive, significant (p < 0.001) and direct impacts on estrogen degradation. This is the first study to suggest that applying microbial inoculants and HA could accelerate estrogen degradation during composting in cold regions. The research outcomes offer a practical reference for managing compost safety, thereby decreasing its potential environmental and human health impacts.

A practical example of exploratory factor analysis and critical realism.

Exploratory factor analysis (EFA) can link the levels of ontology in critical realism together as described in Summers (2020). To demonstrate how components of the actual can be defined by linking potential factors in the empirical to the theorised generative mechanisms in the real. This article describes one part of a three-part sequential mixed-methods study that used EFA to describe how components of the actual were linked using factors in the empirical to the generative mechanisms in the real. The author theorised three generative mechanisms: the perceived impact of continuing professional development (CPD) on patient care, the motivations for undertaking CPD and the perceived barriers to CPD. He used EFA to test factors from the empirical against these generative mechanisms to identify linking components in the actual. This article shows how components of the actual can be defined using EFA. These components are multifactorial and many factors in the empirical are influenced by different generative mechanisms. Being able to articulate and link the different aspects of ontology enables researchers to define theorised generative mechanisms and link theory to practice.

Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.

The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient's motor and cognitive state.

Using exploratory factor analysis to define levels of ontology in critical realism.

Critical realism is often used in mixed-methods research. There are three domains of ontology in critical realism: the empirical, the actual and the real. Exploratory factor analysis is a statistical method used to identify patterns in datasets. To demonstrate how exploratory factor analysis can assist in defining the domains of ontology in critical realism. The different elements of exploratory factor analysis enable researchers to define each domain of ontology in critical realism. The data related to the different factors in the exploratory factor analysis equate to the domain of the empirical. The bonds that link these factors in the empirical are their theoretical link to the real. The strength of these links is determined by the exploratory factor analysis and are the pathways in the actual linking the real to the empirical. Combining all these elements enables researchers to define a theory, as seen by the experiences and influences on that theory. Exploring people's experiences and then combining them into a theory using critical realism and exploratory factor analysis enables researchers to determine the strength of the relationship between the theory and the experiences.

An equivalence approach to the integrative analysis of feature lists

BackgroundAlthough a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of “enough similarity”) between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance–based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit.ResultsIn this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine –as claimed by the authors– and (ii) a collection of pathogenesis–based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package.ConclusionsThe method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists.

An evaluation of reputation with regard to the opportunistic forwarding of messages in VANETs

A mechanism for trust management is required in vehicular ad hoc networks (VANETs) to record the behavior of the vehicles. However, it is a complex matter to follow the historical behavior of the vehicles, since their identities are protected by pseudonyms, which are constantly changing to prevent tracking. In our proposal based on a centralized reputation system, being a vehicle well behaved in the opportunistic forwarding of messages comprises two components: reputation for messages creation and for messages forwarding. The vehicles can forward messages at a given time, and the types of messages that are sent in each transaction may require a vehicle reputation score. In this case, the decision of a vehicle to accept a message is based on the reputation of the sender vehicle that created or forwarded the message. We describe the reputation system through the first three levels of a functional ontology and finite state machines. We evaluate the system by means of simulations in an urban scenario to obtain the reputations of forwarding and generation of messages, varying the number of misbehaving vehicles. It was found that a centralized reputation scheme reflects the behavior of the vehicles insofar as feedback is delivered to the reputation server.

Using Equivalent Classes of an Ontology to Understand Care Pathway in Amyotrophic Lateral Sclerosis.

To understand the home-based difficulties encountered in the health care pathways of patients with Amyotrophic Lateral Sclerosis (ALS), we must annotate a large amount of textual data, from a database created by the ALS Île de France coordination network. For this purpose, we have developed a modular ontology, consisting of four modules, and a semantic annotation tool integrating the created ontology. The specificity of our approach is the creation of equivalent classes at different levels of the ontology. These equivalent classes represent variables of interest allowing a statistical approach and a clinical analysis of comprehension of care pathways ruptures causing.

Network Analysis Reveals TNF as a Major Hub of Reactive Inflammation Following Spinal Cord Injury

Spinal cord injury (SCI) leads to reactive inflammation and other harmful events that limit spinal cord regeneration. We propose an approach for studying the mechanisms at the levels of network topology, gene ontology, signaling pathways, and disease inference. We treated inflammatory mediators as toxic chemicals and retrieved the genes and interacting proteins associated with them via a set of biological medical databases and software. We identified >10,000 genes associated with SCI. Tumor necrosis factor (TNF) had the highest scores, and the top 30 were adopted as core data. In the core interacting protein network, TNF and other top 10 nodes were the major hubs. The core members were involved in cellular responses and metabolic processes, as components of the extracellular space and regions, in protein-binding and receptor-binding functions, as well as in the TNF signaling pathway. In addition, both seizures and SCI were highly associated with TNF levels; therefore, for achieving a better curative effect on SCI, TNF and other major hubs should be targeted together according to the theory of network intervention, rather than a single target such as TNF alone. Furthermore, certain drugs used to treat epilepsy could be used to treat SCI as adjuvants.

Integrating multi-level deep learning and concept ontology for large-scale visual recognition

To support large-scale visual recognition (i.e., recognizing thousands or even tens of thousands of object classes), a multi-level deep learning algorithm is developed to learn multiple deep networks and a tree classifier jointly, where a concept ontology is constructed to organize large numbers of object classes hierarchically in a coarse-to-fine fashion and determine the inter-related learning tasks automatically. Our multi-level deep learning algorithm can: (a) train multiple deep networks simultaneously to achieve more discriminative representations of both coarse-grained groups and fine-grained object classes at different levels of the concept ontology (i.e., learning multiple sets of deep features simultaneously for different tasks); (b) leverage multi-task learning to train more discriminative classifiers for the fine-grained object classes in the same group to enhance their separability significantly and enable inter-class knowledge transferring; and (c) learn multiple deep networks and the tree classifier jointly in an end-to-end fashion. Our experimental results on three image sets have demonstrated that our multi-level deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Background: Gene ontology (GO) is a well-structured knowledge of biological terms that describes roles of genes and their products in a standardized and organized controlled vocabulary format. Over the last decade, many measures are developed to exploit GO advantages to determine semantic similarities between biological entities. Using GO ontologies, there are some constraints that existing GO-based semantic similarity measures try to address them. For instance, (1) edges in a GO graph, do not indicate uniform distances and also have different densities, and (2) ignoring term levels in an ontology makes “shallow annotation” drawback, i.e., two terms with a certain distance near the root of GO graph have equal semantic similarity with two terms with the same distance but far from the root. Methods: Here, we present wAIC, a two-stage hybrid semantic similarity measure using weighted aggregation of information contents. In wAIC, the impact of each common ancestor on semantic similarity value is determined according to the location of the ancestor in the ontology graph. wAIC, also, filters (from annotating term set) terms that are in upper levels of the graph ontology to reduce shallow annotation constraints. Results: Experimental results confirm that the proposed measure is more consistent with major related constraints, such that, wAIC semantic similarity values have more correlation with both sequence similarity values and gene expression based similarity values than state-of-the-art semantic similarity measures. Conclusions: WAIC show using a weighted aggregation of common ancestors is completely consistent with the human perception and can improve accuracy of gene similarity measurement.

A reference ontology approach to support global product-service production

The need to innovate and compete drives organisations to constantly seek new approaches to facilitate business and commerce. As market places become ever more globalised and digital economies grow, these organisations rely more heavily upon systems to design and deliver their products and services. Hence, when developing and operating a global production network the need for systems to interoperate between different domains and contexts within a global production network becomes paramount if organisations are to succeed. This paper puts forwards a reference ontology that has been developed to enable the interoperation of software tools involved in the global production of new product-services systems (PSS). It sets out the levels of the reference ontology, detailing closely the product-service aspects. This has been developed using a formal logic-based approach. An example knowledge base has been created from industrial end user information with queries applied to this to provide a set of results, showing the ability of the reference ontology.

Strong structuration and carbon accounting

PurposeThe purpose of this paper is to utilize the three abstract-concrete levels of ontology of strong structuration theory (strong ST) to examine how, and to what extent, was the development of carbon accounting frameworks at the policy, industry, and organizational levels enabled by external structures as conditions of action, that is, what was the nature of active agency within a field of position-practice relations that led to the development of these frameworks.Design/methodology/approachA case study was undertaken drawing upon interviews that were undertaken between 2008 and 2011 at the industry and organizational levels as well as documentary evidence relating to carbon accounting policy development at the macro, or policy level.FindingsThe parliamentary committee hearings into the development of the carbon price legislation represented fields of position-practice relationships which highlighted the interplay of the internal structures, capabilities and the roles of both power and trust of the agent(s)-in-focus. A meso-level analysis of the Victorian water industry highlighted how it was able to mediate the exercise of power by the macro level through the early adoption of carbon accounting frameworks. At the ontic or micro level of the individual water business, the development of a greenhouse strategy was also the outcome of position-practice relationships which highlighted the interplay of the internal structures and dispositions of the agent(s)-in-focus. The position-practice relationships at both the industry and organizational level were characterized by both soft power and trust.Research limitations/implicationsFuture research could investigate how the withdrawal of the carbon pricing mechanism in Australia has affected the development of carbon accounting practices whilst overseas research could examine the extent to which carbon accounting frameworks were the outcome of position-practice relationships.Practical implicationsGiven the global significance of carbon accounting, this paper provides an overview as to how the early adoption of voluntary carbon accounting practices resulted in a reduction in carbon emissions within the water industry and therefore limited its liability for the carbon price.Originality/valueThis paper illustrates how the strong ST ontological concept of position-practices can be utilized at the macro, meso, and ontic levels and how these relationships mediated the impact of the carbon price upon both the water industry and the individual water business.

Initial teacher education: Does self-efficacy influence candidate teacher academic achievement and future career performance?

This quantitative investigation examined the influence of low and high self-efficacy on candidate teacher academic performance in a foreign language teaching methodology course through testing the speculation that high self-efficacy levels would improve pedagogical-content knowledge (PCK). Positivism guided the research design at the levels of ontology (a single conceptualization of self-efficacy) and epistemology (detachment from rather than interactions with the subjects). The positivist paradigm also guided this study at the methodology level through using a nomothetic research strategy (causal comparative), data collection methods (self-efficacy questionnaire and PCK achievement test), data analysis techniques (independent-groups t-test and its non-parametric counterpart Mann-Whitney U test). Data analysis indicated no significant differences between low and high self-efficacious students in their PCK. The study raised questions about examining self-efficacy through self-reporting measures and cast serious doubts on the positive influence of high self-efficacy on academic achievement.

Interdisciplinary and intercultural differences in learning strategy use: implications for language processing, curriculum and instruction

This investigation examines English as foreign language college interdisciplinary and intercultural differences in learning strategy use and their implications for language processing. Positivism underpins this research at the levels of ontology (standardized variables), epistemology (detachment from the subjects) and methodology, using nomothetic research strategy (survey), instruments (questionnaires) and data analysis techniques (multivariate analysis of variance and analysis of variance). Main findings show interdisciplinary differences between the Humanities and Science majors in student use of compensation strategies in favour of the Science major while finding no intermajor differences in student use of other strategies. The findings indicate no ethnic differences in learning strategy use, since European, American and Eastern ethnicities almost use typical strategies. Therefore, disagreements on alleged unmediated deterministic relationships between ethnicity and cognitive processing remain in place. Instead, curriculum and instructional designs, strategy training and individual differences rather than cultural stereotypes influence language processing. For curriculum and instructional designs to facilitate language processing and pedagogy, effective strategies should underlie national, classroom-level and school-level curriculum developments.