Denture base polymers for analog and digital manufacturing: comparative study of the flexural strength, elastic modulus, and compressive strength of their mechanical properties / Polímeros para fabricación análoga y digital de bases de dentadura: un estudio comparativo de la resistencia flexional, módulo elástico y resistencia a la compresión de sus propiedades mecánicas

ABSTRACT Introduction: the emerging manufacture technologies for dental restorations have brought new materials with them, such as 3D-printing resins and CAD/CAM discs for the manufacturing of denture bases. Currently, there is no rigorous mechanical characterization for these materials in the literature, apart from the ones reported in technical datasheets. Method: samples for mechanical characterization were manufactured with a conventional heat cure acrylic, a CAD/CAM polymethyl methacrylate (PMMA) disc and two 3D-printing resins. The samples were tested in a universal testing machine, according to ISO 20795-1 for flexural strength and elastic modulus. Compression strength was also determined under dry conditions. The average value of each property was calculated (n = 5). One-way ANOVA and Tukey's multiple comparisons tests were used. Results: mean flexural strengths ranged from 78.35±2.99 to 87.48±4.47MPa, elastic moduli were between 2125.43±57.05 and 2277.72±58.46MPa, and compression strengths values ranged from 85.03±2.14 to 119.15±2.87MPa. Statistical analyses showed significant differences for flexural and compression strengths but did not show any difference for elastic moduli. Conclusions: all the tested materials met the minimum required specification for mechanical properties given by ISO 20795-1. From a mechanical point of view, the new materials for digital technologies, i.e., CAD/CAM disc and 3D-printing resins, are suitable for denture-base applications.

RESUMEN Introducción: con la aparición de nuevas tecnologías de manufactura han surgido nuevos materiales, como resinas de impresión 3D y discos CAD/CAM, todos empleados para fabricación de bases de dentadura. Actualmente no se cuenta con caracterizaciones mecánicas rigurosas para estos materiales, salvo lo expresado en fichas técnicas. Método: se fabricaron muestras para caracterización de propiedades mecánicas con un acrílico termopolimerizable convencional, un disco CAD/CAM de polimetilmetacrilato (PMMA) y dos resinas de impresión 3D. Se fallaron las probetas en una máquina universal de ensayos, según lo exigido por la norma ISO 20795-1 para el caso de la resistencia y módulo de flexión. La resistencia a la compresión también fue determinada. Se calculó el valor promedio de cada propiedad (n = 5). Se realizó un análisis de varianza de una vía y un análisis de Tukey para comparaciones múltiples. Resultados: los valores de resistencia a la flexión oscilaron entre 78.35±2.99 y 87.48±4.47MPa; el módulo de flexión estuvo en un rango entre 2125.43±57.05 y 2277.72±58.46MPa. La resistencia a la compresión fluctuó entre 85.03±2.14 y 119.15±2.87MPa. Los análisis estadísticos indicaron diferencias significativas para las resistencias a la flexión y compresión, pero no evidenciaron diferencias para el módulo de flexión. Conclusiones: todos los materiales evaluados cumplieron con la especificación mínima de propiedades mecánicas, dada por la ISO 20795-1. Desde el punto de vista mecánico, los nuevos materiales para las tecnologías digitales, discos CAD/CAM y resinas de impresión 3D, son aptos para su aplicación en manufactura de bases de dentadura.

Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants.

The datasets and code presented in this article are related to the research article entitled "Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets"1. The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.