Correlating tephras and cryptotephras using glass compositional analyses and numerical and statistical methods:Review and evaluation

We define tephras and cryptotephras and their components (mainly ash-sized particles of glass ± crystals in distal deposits) and summarize the basis of tephrochronology as a chronostratigraphic correlational and dating tool for palaeoenvironmental, geological, and archaeological research. We then document and appraise recent advances in analytical methods used to determine the major, minor, and trace elements of individual glass shards from tephra or cryptotephra deposits to aid their correlation and application. Protocols developed recently for the electron probe microanalysis of major elements in individual glass shards help to improve data quality and standardize reporting procedures. A narrow electron beam (diameter ~3-5 μm) can now be used to analyze smaller glass shards than previously attainable. Reliable analyses of ‘microshards’ (defined here as glass shards <32 µm in diameter) using narrow beams are useful for fine-grained samples from distal or ultra-distal geographic locations, and for vesicular or microlite-rich glass shards or small melt inclusions. Caveats apply, however, in the microprobe analysis of very small microshards (<=~5 µm in diameter), where particle geometry becomes important, and of microlite-rich glass shards where the potential problem of secondary fluorescence across phase boundaries needs to be recognised. Trace element analyses of individual glass shards using laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS), with crater diameters of 20 μm and 10 μm, are now effectively routine, giving detection limits well below 1 ppm. Smaller ablation craters (<10 μm) can be subject to significant element fractionation during analysis, but the systematic relationship of such fractionation with glass composition suggests that analyses for some elements at these resolutions may be quantifiable. In undertaking analyses, either by microprobe or LA-ICP-MS, reference material data acquired using the same procedure, and preferably from the same analytical session, should be presented alongside new analytical data. In part 2 of the review, we describe, critically assess, and recommend ways in which tephras or cryptotephras can be correlated (in conjunction with other information) using numerical or statistical analyses of compositional data. Statistical methods provide a less subjective means of dealing with analytical data pertaining to tephra components (usually glass or crystals/phenocrysts) than heuristic alternatives. They enable a better understanding of relationships among the data from multiple viewpoints to be developed and help quantify the degree of uncertainty in establishing correlations. In common with other scientific hypothesis testing, it is easier to infer using such analysis that two or more tephras are different rather than the same. Adding stratigraphic, chronological, spatial, or palaeoenvironmental data (i.e. multiple criteria) is usually necessary and allows for more robust correlations to be made. A two-stage approach is useful, the first focussed on differences in the mean composition of samples, or their range, which can be visualised graphically via scatterplot matrices or bivariate plots coupled with the use of statistical tools such as distance measures, similarity coefficients, hierarchical cluster analysis (informed by distance measures or similarity or cophenetic coefficients), and principal components analysis (PCA). Some statistical methods (cluster analysis, discriminant analysis) are referred to as ‘machine learning’ in the computing literature. The second stage examines sample variance and the degree of compositional similarity so that sample equivalence or otherwise can be established on a statistical basis. This stage may involve discriminant function analysis (DFA), support vector machines (SVMs), canonical variates analysis (CVA), and ANOVA or MANOVA (or its two-sample special case, the Hotelling two-sample T² test). Randomization tests can be used where distributional assumptions such as multivariate normality underlying parametric tests are doubtful. Compositional data may be transformed and scaled before being subjected to multivariate statistical procedures including calculation of distance matrices, hierarchical cluster analysis, and PCA. Such transformations may make the assumption of multivariate normality more appropriate. A sequential procedure using Mahalanobis distance and the Hotelling two-sample T² test is illustrated using glass major element data from trachytic to phonolitic Kenyan tephras. All these methods require a broad range of high-quality compositional data which can be used to compare ‘unknowns’ with reference (training) sets that are sufficiently complete to account for all possible correlatives, including tephras with heterogeneous glasses that contain multiple compositional groups. Currently, incomplete databases are tending to limit correlation efficacy. The development of an open, online global database to facilitate progress towards integrated, high-quality tephrostratigraphic frameworks for different regions is encouraged

The Vehicle, Fall 1991

Table of Contents ImprovisationAmy Schmitzerpage 5-6 Courthouse ClockAnthony Smithpage 7 The PaintingAngie Gallionpage 8 Empty MoonVictoria Bennettpage 9 ClarissaLaura Durnellpage 10 untitledNancy Jamespage 11 Imprisoned (photo)Dan Kooncepage 12 I Hit Mother Nature with My Ten SpeedJohn Haywardpage 13 The Realm of MotherJennifer Moropage 14-16 untitled (drawing)Mark Randallpage 20 With Purity and PerversionBret Evangelistapage 21 O\u27Hare AirportAnthony Smithpage 22 morgen, my desolationtravis mcdadepage 22-23 Ever GreenChris Rosenstockpage 24 JigsawThomas D. Schnarrepage 25-26 Notes on the Egyptian ExhibitVictoria Bennettpage 27 Moving OnChristina Roypage 28 weep my inquisitive hearttravis mcdadepage 29-30 Dance (drawing)Tim Cullotonpage 31 Roots of the OakThomas D. Schnarrepage 32-33 god\u27s suicideLiam Burkepage 34 The Poa TreeSheila Taylorpage 35https://thekeep.eiu.edu/vehicle/1056/thumbnail.jp

Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

BACKGROUND: parent-of-origin effects (POE) play important roles in complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing modifiable POE exist. METHODS: in order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as ‘predicted mRNA expression levels’ of DNA methylation/imprinting machinery genes and environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). FINDINGS: a set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, ‘lifetime smoking status’ and ‘predicted mRNA expression levels’ of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. INTERPRETATION: the modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases. FUNDING: NSFC (81971270),H2020-MSCA-ITN(721815), Wellcome (204979/Z/16/Z,104036/Z/14/Z), MRC (MC_UU_00007/10, MC_PC_U127592696), CSO (CZD/16/6,CZB/4/276, CZB/4/710), SFC (HR03006), EUROSPAN (LSHG-CT-2006-018947), BBSRC (BBS/E/D/30002276), SYSU, Arthritis Research UK, NHLBI, NIH

Haplotype-based association analysis of general cognitive ability in Generation Scotland, the English Longitudinal Study of Ageing, and UK Biobank

Background: Cognitive ability is a heritable trait with a polygenic architecture, for which several associated variants have been identified using genotype-based and candidate gene approaches. Haplotype-based analyses are a complementary technique that take phased genotype data into account, and potentially provide greater statistical power to detect lower frequency variants. Methods: In the present analysis, three cohort studies (ntotal = 48,002) were utilised: Generation Scotland: Scottish Family Health Study (GS:SFHS), the English Longitudinal Study of Ageing (ELSA), and the UK Biobank. A genome-wide haplotype-based meta-analysis of cognitive ability was performed, as well as a targeted meta-analysis of several gene coding regions. Results: None of the assessed haplotypes provided evidence of a statistically significant association with cognitive ability in either the individual cohorts or the meta-analysis. Within the meta-analysis, the haplotype with the lowest observed P-value overlapped with the D-amino acid oxidase activator (DAOA) gene coding region. This coding region has previously been associated with bipolar disorder, schizophrenia and Alzheimer’s disease, which have all been shown to impact upon cognitive ability. Another potentially interesting region highlighted within the current genome-wide association analysis (GS:SFHS: P = 4.09 x 10-7), was the butyrylcholinesterase (BCHE) gene coding region. The protein encoded by BCHE has been shown to influence the progression of Alzheimer’s disease and its role in cognitive ability merits further investigation. Conclusions: Although no evidence was found for any haplotypes with a statistically significant association with cognitive ability, our results did provide further evidence that the genetic variants contributing to the variance of cognitive ability are likely to be of small effect

The genetic landscape of Scotland and the Isles

Britain and Ireland are known to show population genetic structure; however, large swathes of Scotland, in particular, have yet to be described. Delineating the structure and ancestry of these populations will allow variant discovery efforts to focus efficiently on areas not represented in existing cohorts. Thus, we assembled genotype data for 2,554 individuals from across the entire archipelago with geographically restricted ancestry, and performed population structure analyses and comparisons to ancient DNA. Extensive geographic structuring is revealed, from broad scales such as a NE to SW divide in mainland Scotland, through to the finest scale observed to date: across 3 km in the Northern Isles. Many genetic boundaries are consistent with Dark Age kingdoms of Gaels, Picts, Britons, and Norse. Populations in the Hebrides, the Highlands, Argyll, Donegal, and the Isle of Man show characteristics of isolation. We document a pole of Norwegian ancestry in the north of the archipelago (reaching 23 to 28% in Shetland) which complements previously described poles of Germanic ancestry in the east, and "Celtic" to the west. This modern genetic structure suggests a northwestern British or Irish source population for the ancient Gaels that contributed to the founding of Iceland. As rarer variants, often with larger effect sizes, become the focus of complex trait genetics, more diverse rural cohorts may be required to optimize discoveries in British and Irish populations and their considerable global diaspora.</p

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Administrative Managers – A Critical Link

Institutional responses to changes in the higher education environment have caused movements in the roles and identities of administrative managers in UK universities. These shifts have highlighted the problem for individuals of balancing traditional public service considerations of administration with institutional innovation and development. Administrative managers find themselves not only acting as independent arbiters, giving impartial advice on the basis of professional expertise, but also becoming involved in political judgements about institutional futures. They increasingly undertake an interpretive function between the various communities of the university and its external partners. As the boundaries of the university have become more permeable, administrative and academic management have inter-digitated, and hybrid roles have developed. In undertaking increasingly complex functions, therefore, administrative managers play a critical role in linking the academic and executive arms of governance in the university

Structural Characterization of a Novel Chlamydia pneumoniae Type III Secretion-Associated Protein, Cpn0803

Type III secretion (T3S) is an essential virulence factor used by Gram-negative pathogenic bacteria to deliver effector proteins into the host cell to establish and maintain an intracellular infection. Chlamydia is known to use T3S to facilitate invasion of host cells but many proteins in the system remain uncharacterized. The C. trachomatis protein CT584 has previously been implicated in T3S. Thus, we analyzed the CT584 ortholog in C. pneumoniae (Cpn0803) and found that it associates with known T3S proteins including the needle-filament protein (CdsF), the ATPase (CdsN), and the C-ring protein (CdsQ). Using membrane lipid strips, Cpn0803 interacted with phosphatidic acid and phosphatidylinositol, suggesting that Cpn0803 may associate with host cells. Crystallographic analysis revealed a unique structure of Cpn0803 with a hydrophobic pocket buried within the dimerization interface that may be important for binding small molecules. Also, the binding domains on Cpn0803 for CdsN, CdsQ, and CdsF were identified using Pepscan epitope mapping. Collectively, these data suggest that Cpn0803 plays a role in T3S

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts