5 research outputs found
Erbium lasers in periodontology
Get PDFThere are many advantages to using lasers in periodontal therapy, including better visualization of cutting, patient acceptance, and detoxification of a periodontal pocket. Other advantages are less invasive surgery to gain access, minimal wound contraction and scarring. Many of these concepts of laser therapy are positive, although others still require research. There are clearly many favorable applications for lasers in periodontal therapy but further studies are necessary to determine in which procedures laser therapy can be best applied
Nd:YAG lasers in nonsurgical periodontal treatment - a literature review
Get PDFNonsurgical periodontal therapy addresses debriding the area of bacteria, endotoxins, and hard deposits from the root surface to restore gingival health.The instrumentation is focused on the root surface and most often accomplished through manual and power scaling. Nowadays lasers can alsobe used for root debridement
Emdogain (EMD) and platelet-rich plasma (PRP) in periodontal regeneration
Get PDFPeriodontitis is a major cause of adult tooth loss and is characterized by bacteria-induced inflammation and periodontal destruction. The ultimate goal of periodontal therapy is not only to slow down the development of periodontal disease, but also to regenerate the architectural and functional integrity of the periodontal complex, which includes the formation of new cementum together with a new connective tissue attachment between the newly formed bone and cementum.Periodontitis is a major cause of adult tooth loss and is characterized by bacteria-induced inflammation and periodontal destruction. The ultimate goal of periodontal therapy is not only to slow down the development of periodontal disease, but also to regenerate the architectural and functional integrity of the periodontal complex, which includes the formation of new cementum together with a new connective tissue attachment between the newly formed bone and cementum
OpenBiodiv-O Ontology: Bridging the Gap Between Biodiversity Data and Biodiversity Publishing
No full textCommunication of research findings is the last and arguably the most influential step of the scientific process. This is especially true for biodiversity science, in which new species descriptions and introduction of new taxonomic names happens through publication, as governed by the International Codes. Despite the strict rules for naming new taxa and revising existing taxonomic nomenclatures within scholarly literature, there is no system for keeping track of these changes and information often remains locked within the text of thousands of scattered journal articles. This talk presents OpenBiodiv-O, the first ontology which conceptually models the biodiversity publishing domain and through its application in the semantic graph database OpenBiodiv contributes to knowledge management of this domain.
In combination with already existing ontologies for biodiversity and publishing (e.g. DarwinCore-based ontologies, SPAR ontologies), resource types introduced by OpenBiodiv-O help to create a link between these two domains. The ontology models the general structure of a research article, including sections specific to taxonomic articles, such as the treatment section, as well as other conceptual entities from taxonomy, like scientific names and taxonomic concepts. Thus, OpenBiodiv-O links scientific names to the corresponding article section in which they are mentioned via the class Taxonomic Name Usage and helps to discover hidden relationships between names. In addition, OpenBiodiv-O models the article metadata, such as the author names, affiliations and unique identifiers. The orcid class from the recently introduced Datacite ontology within OpenBiodiv-O models the ORCID of authors and will enable the future disambiguation of authors and linking with other platforms using ORCID.
OpenBiodiv-O has been applied to the biodiversity knowledge graph OpenBiodiv, which is based on a Linked Open Dataset, created from Pensoft's journal articles and Plazi's treatments. Publishing of semantically enhanced scholarly literature as XML enables the conversion of semi-structured narrative into connected Resource Description Framework (RDF) statements. The ontology serves as a skeleton for the transformation of more than 729 million statements into a Linked Open Dataset. Reusing of existing ontologies within OpenBiodiv-O helps to establish a link between OpenBiodiv-O and other ontologies and facilitates federated querying between OpenBiodiv and other knowledge graphs. The application of OpenBiodiv-O towards a working solution for the biodiversity publishing domain demonstrates the potential of ontology modelling for data organisation and management
Data Auditing, Cleaning and Quality Assurance Workflows from the Experience of a Scholarly Publisher
No full textData publishing became an important task in the agenda of many scholarly publishers in the last decade, but far less attention has been paid to the actual reviewing and quality checking of the published data. Quality checks are often being delegated to the reviewers of the article narrative, many of whom may not be qualified to provide a professional data review. The talk presents the workflows developed and used by Pensoft journals to provide data auditing, cleaning and quality assurance. These are:
Data auditing/cleaning workflow for datasets published as data papers (Fig. 1, see also this blog). All datasets undergo an audit for compliance with a data quality checklist prior to peer-review. The author is provided with an audit report and is asked to correct the data flaws and consider other recommendations in the report. This check is conducted regardless of whether the datasets are provided as supplementary material within the data paper manuscript or linked from the Global Biodiversity Information Facility (GBIF) or another repository. The manuscript is not forwarded to peer review until the author corrects the data associated with it. This workflow is applied in all journals of the publisher's portfolio, including Biodiversity Data Journal, ZooKeys, PhytoKeys, MycoKeys and others.
Automated check and validation of data within the article narratiive in the Biodiversity Data Journal provided during the authoring process in the ARPHA Writing Tool (AWT), and consequently, during the peer review process in the journal. Among others, the automated validation tool checks for compliance with the biological Codes (for example, a new species description cannot be submitted without designation of a holotype and the respective specimen record).
Check for consistency and validation of the full-text JATS XML against the TaxPub XML schema. This quality check ensures a succesfull full-text submission and display on PubMedCentral, extraction of taxon treatments and their visualisation on Plazi's TreatmentBank and GBIF, indexing in various data aggregators, and so on.
Human-provided quality check of the mass automated extraction of taxon treatments from legacy literature via the GoldenGate-Imagine workflow developed by Plazi and implemented for the purposes of the Arcadia project in a collaboration with Pensoft.
Putting high-quality data in valid XML formats (Pensoft's JATS and Plazi's TaxonX) into a machine readable semantic format (RDF) to guarantee efficient extraction and transformation. Data from Pensoft's journals and Plazi's treatments are uploaded as semantic triples into the OpenBiodiv Biodiversity Knowledge Graph where it is modeled according to the OpenBiodiv-O ontology (Senderov et al. 2018). Semantic technologies facilitate the mapping of scientific names in OpenBiodiv to GBIF's taxonomic backbone and the addressing of complex biodiversity questions.
We have realised in the course of many years experience in data publishing that data quality checking and assurance testing requires specific knowledge and competencies, which also vary between the various methods of data handling and management, such as relational databases, semantic XML tagging, Linked Open Data, and others. This process cannot be trusted to peer reviewers only and requires the participation of dedicated data scientists and information specialists in the routine publishing process. This is the only way to make the published biodiversity data, such as taxon descriptions, occurrence records, biological observations and specimen characteristics, truly FAIR (Findable, Accessible, Interoperable, Reusable), so that they can be merged, reformatted and incorporated into novel and visionary projects, regardless of whether they are accessed by a human researcher or a data-mining process
