Taxonomy Evolution Research Articles

Exploring the Genomic Landscape of Hepatobiliary Cancers to Establish a Novel Molecular Classification System.

Taxonomy of hepatobiliary cancer (HBC) categorizes tumors by location or histopathology (tissue of origin, TO). Tumors originating from different TOs can also be grouped by overlapping genomic alterations (GA) into molecular subtypes (MS). The aim of this study was to create novel HBC MSs. Next-generation sequencing (NGS) data from the AACR-GENIE database were used to examine the genomic landscape of HBCs. Machine learning and gene enrichment analysis identified MSs and their oncogenomic pathways. Descriptive statistics were used to compare subtypes and their associations with clinical and molecular variables. Integrative analyses generated three MSs with different oncogenomic pathways independent of TO (n = 324; p < 0.05). HC-1 "hyper-mutated-proliferative state" MS had rapidly dividing cells susceptible to chemotherapy; HC-2 "adaptive stem cell-cellular senescence" MS had epigenomic alterations to evade immune system and treatment-resistant mechanisms; HC-3 "metabolic-stress pathway" MS had metabolic alterations. The discovery of HBC MSs is the initial step in cancer taxonomy evolution and the incorporation of genomic profiling into the TNM system. The goal is the development of a precision oncology machine learning algorithm to guide treatment planning and improve HBC outcomes. Future studies should validate findings of this study, incorporate clinical outcomes, and compare the MS classification to the AJCC 8th staging system.

Special issue on fuzzy ontologies and fuzzy markup language applications

Latest research on formal methods for knowledge representation based on semantic web technologies witnessed how ontologies have become a fundamental and critical component for developing applications in different real world scenarios. Indeed, ontologies formally model application domains to enable reuse of knowledge and allow people and software agents to share a common understanding of the structure of information. The body of a knowledge modeled through an ontological approach is based on the conceptualization idea, i.e., an abstract and simplified view of the knowledge related to given application scenario. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real world applications. Soft computing techniques can enhance ontological knowledge representation by providing methods for directly dealing with imprecision and vagueness. Indeed, fuzzy set theory may extend the conventional ontology idea with a collection of fuzzy or vague terms like ‘‘creamy’’, ‘‘hot’’, ‘‘large’’, for which a clear and precise definition is not possible. In this scenario, the fuzzy markup language (FML) represents an important result because it allows fuzzy scientists to express their ‘‘imprecise’’ ideas in abstract and interoperable way by improving their productivity and, at the same time, increasing the average quality of their works. The objective of this special issue is to highlight an ongoing research on fuzzy and FML approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains. This volume contains seven papers that consider different aspects of research on fuzzy ontologies and FML. The first four papers describe the applications exploiting fuzzy theory and ontology. Updating Generalized Association Rules with Evolving Fuzzy Taxonomies by WenYang Lin, Ja-Hwung Su, and Ming-Cheng Tseng proposes an algorithm for mining generalized association rules with fuzzy taxonomic structures where the taxonomy may change as time passes; as shown by their empirical evaluations, the proposed algorithms yield high performances even in high degree of taxonomy evolution. Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents by David T. Parry and Tsung-Chun Tsai describes an approach to identifying subsets of medical knowledge contained in the SNOMED CT dictionary via a crowdsourcing technique. The research aims to assist clinicians in coding small freetext documents such as radiology reports. Fuzzy Ontologies-based User Profiles applied to enhance e-Learning Activities by Mateus Ferreira-Satler, Francisco P. Romero, Victor H. Menendez-Dominguez, Alfredo Zapata and Manuel E. Prieto shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-Learning environments. Decision making with a fuzzy ontology by Christer Carlsson, Matteo Brunelli and Jozsef Mezei shows how soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy G. Acampora (&) Department of Computer Science, University of Salerno, Salerno, Italy e-mail: gacampora@unisa.it

Updating generalized association rules with evolving fuzzy taxonomies

Mining generalized association rules with fuzzy taxonomic structures has been recognized as an important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the membership degrees expressing the fuzzy classification may also need to be adjusted. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called FDiff_ET and FDiff_ET*, to update the discovered generalized frequent itemsets. Empirical evaluations show that our algorithms can maintain their performance even in high degree of taxonomy evolution, and are significantly faster than applying the contemporary fuzzy generalized association mining algorithm FGAR to the database with evolving taxonomy.

The genus Scalida Hebard (Blattaria: Blattellidae,Blattellinae) in China

The blattellid cockroach genus Scalida Hebard, 1929 is revised and redefined on the basis of all species currently included in it, including generic diagnosis, evolution of taxonomy, species list, and taxonomy. Two new species are described and illustrated from China: Scalida quadrispinata and S. pyrrhocephala. Scalida biclavata Bey-Bienko and S. ectobiodes (Saussure) are redescribed and illustrated. A key to all Chinese species is provided.

Incremental maintenance of generalized association rules under taxonomy evolution

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of items remained unchanged, disregarding the fact that the taxonomy might be updated as new transactions are added to the database over time. If this happens, effectively updating the generalized association rules to reflect the database change and related taxonomy evolution is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called IDTE and IDTE2, which can incrementally update the generalized association rules when the taxonomy of items evolves as a result of new transactions. Empirical evaluations show that our algorithms can maintain their performance even for large numbers of incremental transactions and high degrees of taxonomy evolution, and are faster than applying contemporary generalized association mining algorithms to the whole updated database.

Lattice-based dynamic and overlapping taxonomies: The case of epistemic communities

We present a method for describing taxonomy evolution. We focus on the structure of epistemic communities (ECs), or groups of agents sharing common knowledge concerns. Introducing a formal framework based on Galois lattices, we categorize ECs in an automated and hierarchically structured way and propose criteria for selecting the most relevant epistemic communities - for instance, ECs gathering a certain proportion of agents and thus prototypical of major fields. This process produces a manageable, insightful taxonomy of the community. Then, the longitudinal study of these static pictures makes possible an historical description. In particular, we capture stylized facts such as field progress, decline, specialization, interaction (merging or splitting), and paradigm emergence. The detection of such patterns in epistemic networks could fruitfully be applied to other contexts.

Taxonomic triage and the poverty of phylogeny.

Revisionary taxonomy is frequently dismissed as merely descriptive, which belies its strong intellectual content and hypothesis-driven nature. Funding for taxonomy is inadequate and largely diverted to studies of phylogeny that neither improve classifications nor nomenclature. Phylogenetic classifications are optimal for storing and predicting information, but phylogeny divorced from taxonomy is ephemeral and erodes the accuracy and information content of the language of biology. Taxonomic revisions and monographs are efficient, high-throughput species hypothesis-testing devices that are ideal for the World Wide Web. Taxonomic knowledge remains essential to credible biological research and is made urgent by the biodiversity crisis. Theoretical and technological advances and threats of mass species extinctions indicate that this is the time for a renaissance in taxonomy. Clarity of vision and courage of purpose are needed from individual taxonomists and natural history museums to bring about this evolution of taxonomy into the information age.

Victims of Domestic Violence: A Proposal for a Community Diagnosis Based on One of Two Domains of NANDA Taxonomy II

PURPOSETo explore and identify diagnostic components to amplify NANDA nursing diagnoses by modifying the root violence. Whereas violence is nondebatable as a diagnostic concept in nursing, other alternatives have not been identified in the two existing diagnoses.METHODSUsing the case study method, this qualitative study sought to identify commonalties in a population of women who were “donnas da casa” (homemakers) in a small rural community of approximately 100 families, typical of the Brazilian northeast. The sample of 7 women was identified through a larger study that had been based on health needs of the community. Data were obtained through observation during a home visit and a semistructured interview based on NANDA Taxonomy II. Observations were focused on hygiene, manner of dress, home environment, and physical and emotional state. Data were analyzed by content and clustered into major categories. From these a profile of the women and another of the partners emerged.FINDINGSSubjects ranged in age from 33 to 43 years, and number of children between 3 and 7. One of the 7 women was literate; 5 were underweight; all were slovenly attired. They appeared sad and older than their age. The majority seemed relieved to unburden themselves to the interviewers as they went through a gamut of emotions such as sadness, anguish, and irritability expressed through crying, restlessness, changes in body language, and tone of voice. The shortage of beds was supplemented by hammocks and mats or cardboard.The women spoke of being confined to their home and of male partners who drank on weekends, thus leaving them with little money for necessities of life. There were accounts of beatings when the partner returned home after drinking, overt nonacceptance of children from previous marriages, and general destruction of the family environment. New children were regarded as just another mouth to feed.DISCUSSIONThe profiles pointed to the necessity of identifying a new nursing diagnosis that would be linked, only tangentially, by the root violence to the two diagnoses in NANDA Taxonomies I and II. This insight led us to consider that a new method of listing NANDA diagnoses, by root only, is imperative in the evolution of Taxonomy II. Proposed descriptors, Victims of (Axis 3) and Domestic (Axis 6) would be identified by Axes, thereby facilitating the process of classifying in the Domains and Classes. The two existing NANDA diagnoses, risk for other‐directed violence and risk for self‐directed violence, are proposed for classification in Class 3, Violence, in Domain 11 of Taxonomy II. Safety/Protection could, by virtue of their modification power, find anchor in another domain such as Domain 6, Self‐Perception.CONCLUSIONSAlthough Safety/Protection seems the most logical domain for classification by root, the axes, dimensions of human responses, could pull the diagnosis in another direction, thereby dictating other nursing interventions and nursing outcomes