CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade

10.1371/journal.pone.0047406PLoS ONE712

Predicting site index from climate and soil variables for cork oak (Quercus suber L.) stands in Portugal

Site productivity, assessed through site index, was modelled using partial least squares regression as a function of soil and climatic variables. Two alternative models were developed: a full model, considering all available explanatory variables, and a reduced model, considering only variables that can be obtained without digging a soil pit. The reduced model was used for mapping the site index distribution in Portugal, on the basis of existing digital cartography available for the whole country. The developed models indicate the importance of water availability and soil water holding capacity for site index value distribution. Site index was related to climate, namely evaporation and frost, and soil characteristics such as lithology, soil texture, soil depth, thickness of the A horizon and soil classification. The variability of the estimated values within the map (9.5–16.8 m with an average value of 13.4 m) reflects the impact of soil characteristics on the site productivity estimation. These variables should be taken into consideration during the establishment of new plantations of cork oak, and management of existing plantations. Results confirm the potential distribution of cork oak in coastal regions. They also suggest the existence of a considerable area, located both North and South of the Tagus river, where site indices values of medium (]13;15]) to high (]15;17]) productivity classes may be expected. The species is then expected to be able to have good productivity along the northern coastal areas of Portugal, where presently it is not a common species but where, according to historical records, it occurred until the middle of the sixteenth century. The present research focused on tree growth. Cork growth and cork quality distribution needs to be further researched through the establishment of long term experimental sites along the distribution area of cork oak, namely in the central and northern coastal areas of the countryinfo:eu-repo/semantics/publishedVersio

Tallo: A global tree allometry and crown architecture database.

This is the final version. Available from Wiley via the DOI in this record. Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.Natural Environment Research Council (NERC)Natural Environment Research Council (NERC); Ministry of Education, Youth and Sports of the Czech RepublicFAPEMIGUniversidad Nacional Autónoma de MéxicoUniversidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y TecnologíaSwedish Energy AgencyUKRIFederal Ministry of Education and ResearchNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Science FoundationNational Science FoundationInternational Foundation for ScienceP3FACDynAfForNanjing Forestry UniversityJiangsu Science and Technology Special ProjectHebei UniversityAgence Nationale de la RechercheAgence Nationale de la RechercheAgua Salud ProjectU.S. Department of EnergyCAPE

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information