Studies on the preparation, properties and analysis of high purity yttrium oxide and yttrium metal at the Ames Laboratory

The research and development work carried out at the Ames Laboratory on the chemistry and metallurgy of yttrium is described in detail in this report or companion reports to which references are herein made. Discussions of the separation of yttrium from the rare-earth elements by ion exchange, of comprehensive investigations of the preparation of yttrium fluoride, and of various ways of reducing the fluoride to the metallic state are presented. Chemical and spectrographic methods of analyzing yttrium and its compounds for oxygen and other impurities are described and comparisons made between the different methods. A pilot plant process for producing tonnage quantities of yttrium metal is presented with detailed descriptions of the equipment and operating procedures employed. The complete process entails the extraction of an yttrium and rare earth mixture from xenotime sand, separation of the yttrium from this mixture in thirty-inch-diameter columns, hydrofluorination of the resulting oxide and its subsequent reduction to the metal. The basic metal process consists of the reduction of yttrium fluoride with calcium, forming a low melting yttrium-magnesium alloy. The magnesium is subsequently removed by vacuum sublimation, producing a porous yttrium product. This is consolidated by vacuum arc melting into a six-inch-diameter ingot. Quantities of high purity yttrium metal were prepared by vacuum distillation and by-a sa,lt extraction refining process. Yttrium metal containing 100 to 300 ppm oxygen is soft, ductile and easily fabricated at room temperature

IUPHAR-DB: updated database content and new features

The International Union of Basic and Clinical Pharmacology (IUPHAR) database, IUPHAR-DB (http://www.iuphar-db.org) is an open access, online database providing detailed, expert-driven annotation of the primary literature on human and rodent receptors and other drug targets, together with the substances that act on them. The present release includes information on the products of 646 genes from four major protein classes (G protein-coupled receptors, nuclear hormone receptors, voltage- and ligand-gated ion channels) and ∼3180 bioactive molecules (endogenous ligands, licensed drugs and key pharmacological tools) that interact with them. We have described previously the classification and curation of data for small molecule ligands in the database; in this update we have annotated 366 endogenous peptide ligands with their amino acid sequences, post-translational modifications, links to precursor genes, species differences and relationships with other molecules in the database (e.g. those derived from the same precursor). We have also matched targets with their endogenous ligands (peptides and small molecules), with particular attention paid to identifying bioactive peptide ligands generated by post-translational modification of precursor proteins. Other improvements to the database include enhanced information on the clinical relevance of targets and ligands in the database, more extensive links to other databases and a pilot project for the curation of enzymes as drug targets

Landscape science: a Russian geographical tradition

The Russian geographical tradition of landscape science (landshaftovedenie) is analyzed with particular reference to its initiator, Lev Semenovich Berg (1876-1950). The differences between prevailing Russian and Western concepts of landscape in geography are discussed, and their common origins in German geographical thought in the late nineteenth and early twentieth centuries are delineated. It is argued that the principal differences are accounted for by a number of factors, of which Russia's own distinctive tradition in environmental science deriving from the work of V. V. Dokuchaev (1846-1903), the activities of certain key individuals (such as Berg and C. O. Sauer), and the very different social and political circumstances in different parts of the world appear to be the most significant. At the same time it is noted that neither in Russia nor in the West have geographers succeeded in specifying an agreed and unproblematic understanding of landscape, or more broadly in promoting a common geographical conception of human-environment relationships. In light of such uncertainties, the latter part of the article argues for closer international links between the variant landscape traditions in geography as an important contribution to the quest for sustainability

Evaluating species distribution model predictions through time against paleozoological records.

Species distribution models (SDMs) are widely used to project how species distributions may vary over time, particularly in response climate change. Although the fit of such models to current distributions is regularly enumerated, SDMs are rarely tested across longer time spans to gauge their actual performance under environmental change. Here, we utilise paleozoological presence/absence records to independently assess the predictive accuracy of SDMs through time. To illustrate the approach, we focused on modelling the Holocene distribution of the hartebeest, Alcelaphus buselaphus, a widespread savannah-adapted African antelope. We applied various modelling algorithms to three occurrence datasets, including a point dataset from online repositories and two range maps representing current and 'natural' (i.e. hypothetical assuming no human impact) distributions. We compared conventional model evaluation metrics which assess fit to current distributions (i.e. True Skill Statistic, TSSc, and Area Under the Curve, AUCc) to analogous 'paleometrics' for past distributions (i.e. TSSp, AUCp, and in addition Boycep, F2-scorep and Sorensenp). Our findings reveal only a weak correlation between the ranking of conventional metrics and paleometrics, suggesting that the models most effectively capturing present-day distributions may not be the most reliable to hindcast historical distributions, and that the choice of input data and modelling algorithm both significantly influences environmental suitability predictions and SDM performance. We thus advocate assessment of model performance using paleometrics, particularly those capturing the correct prediction of presences, such as F2-scorep or Sorensenp, due to the potential unreliability of absence data in paleozoological records. By integrating archaeological and paleontological records into the assessment of alternative models' ability to project shifts in species distributions over time, we are likely to enhance our understanding of environmental constraints on species distributions

Electrical Resistivity of Lanthanum, Praseodymium, Neodymium, and Samarium

The electrical resistivities of polycrystalline samples of La, Pr, Nd, and Sm are reported in the temperature range 1.3 to 300 deg K. La exhibits a superconducting transition at 5.8 deg K. The curve for Pr has slope changes at 61 and 95 deg K. The Nd curve shows small jumps at 5 and 20 deg K. Sm shows slope changes at 14 and 106 deg K. (auth

Decolonial education and geography: Beyond the 2017 Royal Geographical Society with the Institute of British Geographers annual conference

This review is inspired by the recent resurgence of grassroots movements aimed at the decolonisation of education. The departure point of the paper are the numerous, recent academic responses to campaigns such as Rhodes Must Fall, Why is My Curriculum White?, Why Isn't My Professor Black?, and #LiberateMyDegree. Following from there, the narrative is divided into two sections. The first part reviews theoretical approaches to decolonial education, especially those rooted in the modernity/coloniality/decoloniality paradigm. The second part analyses the ways in which geographers have applied these ideas to our discipline. The review pays particular attention to the 2017 Royal Geographical Society with the Institute of British Geographers annual conference, curated under the “Decolonising geographical knowledges” theme. I argue that as geographers, we have to continue reflecting on the meaning of decolonial praxis, especially in relation to geographical education, beyond the recent conference. To these ends, the review concludes with seven specific questions for geographers to consider in the near future

The Concise Guide to PHARMACOLOGY 2023/24:Introduction and Other Protein Targets

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and about 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16176. In addition to this overview, in which are identified 'Other protein targets' which fall outside of the subsequent categorisation, there are six areas of focus: G protein-coupled receptors, ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.</p

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio