A New Approach to Characterizing Deposit Type Using Mineral Inclusion Assemblages in Gold Particles

2021 ◽  
Author(s):  
R. J. Chapman ◽  
J. K. Mortensen ◽  
M. M. Allan ◽  
R. D. Walshaw ◽  
J. Bond ◽  
...  
Keyword(s):  
Unknown Origin ◽  
Hydrothermal Systems ◽  
Mineral Inclusion ◽  
Canadian Cordillera ◽  
Data Sets ◽  
Simple Method ◽  
Gold Particles ◽  
Deposit Type ◽  
Placer Deposits ◽  
Detrital Particles

Abstract Mineral inclusions within native gold are features of lode gold occurrences that are preserved in detrital particles. Inclusion assemblages in populations of gold particles in placers from specific localities are revealed through inspection of polished sections, and assimilation of robust data sets permits reconstruction of the lode source mineralogy. Inclusion assemblages differ considerably according to the source deposit type, and various approaches have been employed to graphically represent inclusion mineralogy. We present a simple method for depicting and comparing inclusion assemblages using a single standardized radar diagram template that illustrates the proportions of 11 metal and 5 nonmetal (and metalloid) elements in each inclusion assemblage. The Canadian Cordillera hosts many different gold-bearing deposit types and is an ideal terrane in which to develop a globally applicable methodology. Although placer gold is widespread, the location and nature of source mineralization is commonly unclear. This study is based on the inclusion suites recorded in 37 sample sets of gold particles from both placer and lode localities. Radar diagrams describing inclusion assemblages show clear generic differences according to deposit type. Diagnostic signatures have been established and act as templates against which samples of unknown origin may be compared. This approach permits differentiation between populations of gold particles formed in different magmatic systems (low-sulfidation epithermal, calc-alkalic porphyry, and alkalic porphyry), which may all be distinguished from gold formed in orogenic (amagmatic) mineralization. Metallic element signatures are most useful in differentiating gold from different magmatic hydrothermal systems, whereas nonmetallic elements allow for classification of orogenic gold subtypes. Comparisons of mineral inclusion signatures from gold in the Canadian Cordillera with samples from similar geologic settings worldwide suggest that this approach to gold fingerprinting is globally applicable. Therefore, the geochemical signatures of inclusion assemblages provide a robust indication of deposit type and may be applied in exploration to illuminate regional metallogeny in areas where relationships between placer deposits and their source(s) may be unclear.

scholarly journals Intragenic Conflict in Phylogenomic Data Sets

2020 ◽  
Vol 37 (11) ◽  
pp. 3380-3388
Author(s):  
Stephen A Smith ◽  
Nathanael Walker-Hale ◽  
Joseph F Walker
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Evolutionary History ◽  
Phylogenetic Signal ◽  
Phylogenetic Analyses ◽  
Simulated Data ◽  
Data Sets ◽  
Alignment Error ◽  
Biological Processes ◽  
Simple Method

Abstract Most phylogenetic analyses assume that a single evolutionary history underlies one gene. However, both biological processes and errors can cause intragenic conflict. The extent to which this conflict is present in empirical data sets is not well documented, but if common, could have far-reaching implications for phylogenetic analyses. We examined several large phylogenomic data sets from diverse taxa using a fast and simple method to identify well-supported intragenic conflict. We found conflict to be highly variable between data sets, from 1% to >92% of genes investigated. We analyzed four exemplar genes in detail and analyzed simulated data under several scenarios. Our results suggest that alignment error may be one major source of conflict, but other conflicts remain unexplained and may represent biological signal or other errors. Whether as part of data analysis pipelines or to explore biologically processes, analyses of within-gene phylogenetic signal should become common.

Multiple regression modeling for estimating endocranial volume in extinct Mammalia

Paleobiology ◽  
2013 ◽  
Vol 39 (1) ◽  
pp. 149-162 ◽  
Author(s):  
Laura C. Soul ◽  
Roger B. J. Benson ◽  
Vera Weisbecker
Keyword(s):  
Multiple Regression ◽  
Brain Size ◽  
Mammalian Brain ◽  
Data Sets ◽  
Full Potential ◽  
Crown Group ◽  
Simple Method ◽  
Phylogenetically Independent Contrasts ◽  
Extant Species ◽  
Endocranial Volume

The profound evolutionary success of mammals has been linked to behavioral and life-history traits, many of which have been tied to brain size. However, studies of the evolution of this key trait have yet to explore the full potential of the fossil record, being limited by the difficulty of obtaining endocranial data from fossils. Using measurements of endocranial volume, length, height, and width of the braincase in 503 adult specimens from 199 extant species, representing 99 of 133 extant mammalian families, we expand upon a simple method of using multiple regression to develop a formula for estimating brain size from external skull measurements. We also examined non-mammalian synapsids to assess the phylogenetic limits of our model's application. Model-predicted volume correlates strongly with measured volume (R2 = 0.993) and prediction error is between 16% and 19%. Error decreases if models developed for well-sampled subclades such as primates or rodents are used, demonstrating that some differential evolution of the relationship between brain size and skull size has occurred. However, reanalysis using phylogenetically independent contrasts demonstrates weak phylogenetic dependency, indicating that our model is appropriate for estimating the endocranial volume of species of unknown phylogenetic affinity. Thus, the model represents a generally applicable, fast and cost-efficient way to dramatically expand the taxonomic and temporal scope of mammalian brain size data sets. Even endocranial volumes of taxa with highly derived crania, such as cetaceans and monotremes, can be estimated confidently. However, the model works best for generalized placental crania. Fundamental differences in cranial architecture suggest that the model cannot provide accurate estimates of endocranial volume in non-mammalian synapsids more basal than Morganucodon (ca. 200 Ma). Therefore, use of the model for taxa phylogenetically distant from the mammalian crown group is not warranted, but it might be used to establish relative brain sizes between closely related subgroups.

scholarly journals A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

2021 ◽  
Vol 12 ◽  
Author(s):  
Osval A. Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Brandon A. Mosqueda-González ◽  
Alison R. Bentley ◽  
Morten Lillemo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Plant Breeding ◽  
Deep Neural Networks ◽  
Reference Population ◽  
Calibration Method ◽  
Data Sets ◽  
Simple Method ◽  
Continuous Response ◽  
Predicting Performance

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

scholarly journals Reference-free deconvolution of DNA methylation data and mediation by cell composition effects

2016 ◽  
Author(s):  
E. Andres Houseman ◽  
Molly L. Kile ◽  
David C. Christiani ◽  
Tan A. Ince ◽  
Karl T. Kelsey ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Reference Data ◽  
Cell Types ◽  
Data Sets ◽  
Phenotypic Data ◽  
Simple Method ◽  
Cell Composition ◽  
Composition Effects ◽  
Heterogeneous Tissues ◽  
Cell Proportion

AbstractWe propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We have demonstrated these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types. Thus the methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.

scholarly journals The Indonesian Titanium Deposit Types and Their Resources: The Aspects For Titanium Commodity Development

2013 ◽  
Vol 8 (3) ◽  
pp. 100-109
Author(s):  
Armin Tampubolon
Keyword(s):  
Granitic Rocks ◽  
The South ◽  
Processing Technologies ◽  
Economic Significance ◽  
North Sulawesi ◽  
Genetic Types ◽  
Metamorphic Basement ◽  
Deposit Type ◽  
Placer Deposits ◽  
Available Resources

Indonesia has several Titanium deposit types: beach placer, alluvial and lateritic types. The source of Titanium minerals in beach placer is derived from Tertiary Volcanic Rocks.Theoccurence of alluvial Titanium deposit is closely associated with cassiterite (tin minerals) alluvial in Bangka-Belitung Islands. The source of Titanium minerals (ilmenite) are derived fromweathering on tin mineralization hosted in metamorphic and granitic rocks within uplifted morphology areas. Lateritic Titanium deposits are formed by lateritization process in association with bauxite and nickel in Riau, Kalimantan and Sulawesi Islands. The sources of Titanium are in granitic and metamorphic basement rocks.Java beach placer resources are quite big in tonnage, nearly 50 million tons in total, with significant TiO  contents ranging from 8.91 % to 3.17 %. Beach placer along the south shoreline of Java seems to indicate consistency in their mineralogical and genetic types with relativelyhomogenous in TiO  contents. Sumatra shows significant resources for lateritic Titanium deposit that is up to 107,800,859 tons with 0.5% to 15 % TiO . The quite interesting resources are indicated by very significant TiO average (15%) with the total of 19,243,757 tons of lateritic deposit type in Bintan Island, Riau,Sumatra.  The total resources of beach placer type deposits in Sumatra are much  lower than lateritic ones.Sulawesi has both beach placer and lateritic types where the interesting resources of this island are indicated by quite big resources and significant values in TiO  grades in North Sulawesi; the tonnage is up to 31,400,000 tons with TiO  content average is 9.85 %.  On the basis of resources and processing technologies, beach placer deposits in Sumatera, Java and Sulawesi are thought to have potentially economic significance. However, such available resources are needed to upgrade by performing detailed and systematic exploration.Government policies and environment concerns are important aspects for consideration in Titanium commodity development of this country.

Unraveling Zr/Hf fractionation: Hydrothermal zirconium and hafnium fluoride complexation up to 400°C

2020 ◽  
Author(s):  
Anselm Loges ◽  
Marion Louvel ◽  
Max Wilke ◽  
Sthephan Klemme ◽  
Timm John ◽  
...  
Keyword(s):  
Hydrothermal Systems ◽  
Effect Of Temperature ◽  
Data Sets ◽  
Strong Temperature Dependence ◽  
Aqueous Solvent ◽  
Fluoride Complexation ◽  
Fluoride Complexes ◽  
The Stability ◽  
Hydrothermal Environments ◽  
Aqueous Complexes

<p>High field strength elements (HFSE) such as Zr and Hf are relatively insoluble in most natural hydrothermal solutions and consequently immobile in most geological systems. However, fluoride forms stable aqueous complexes with many HFSE ions, including Zr<sup>4+</sup> and Hf<sup>4+</sup>, and is thus a potent mobilizer of these elements. Due to their identical charge and similar ionic radius (590 pm and 580 pm, respectively), Zr and Hf behave almost identically in geological system and are therefore referred to as geochemical twins. Fluoride complexation in hydrothermal environments is one of few processes in the Earth's crust that can effectively fractionate them from one another. This fact can be used to trace past fluoride activity in fossil hydrothermal systems by investigating Zr/Hf ratios, if fluoride complexation of Zr and Hf is sufficiently well understood. Mobility of metals as complexes is controlled by two distinct but related mechanisms: Formation of the complex itself and solvation of that complex in the solvent. Poly(hydrogen-fluoride) bridging of fluoride complexes to the surrounding aqueous solvent is crucial to the understanding of the solvation and therefore the mobility of fluoride complexes.</p><p>We report geometries of Zr and Hf fluoride complexes up to 400°C, determined by extended X-Ray absorption fine structure (EXAFS) in a hydrothermal autoclave. Existing data sets on the stability of those complexes at lower temperatures are extended to 400°C. Our data show strong temperature dependence of the complex stability for both metals. However, the effect of temperature is not equally strong for Zr and Hf. Fractionation of the twin pair is thus a function of temperature as well as fluoride activity.</p>

A simple method to extract specific signature genes and relevant algorithms of gene clustering from pangenomic microarray data sets

2006 ◽  
Vol 164 ◽  
pp. S296
Author(s):  
Carine Lambert ◽  
Catherine Spire ◽  
Dominique Bazot ◽  
Andre Guillouzo ◽  
Nancy Claude
Keyword(s):  
Microarray Data ◽  
Gene Clustering ◽  
Data Sets ◽  
Simple Method ◽  
Signature Genes

scholarly journals A simple method of absorption and decay correction in intensities measured by area-detector X-ray diffractometer

1987 ◽  
Vol 20 (3) ◽  
pp. 243-245 ◽  
Author(s):  
F. Takusagawa
Keyword(s):  
Numerical Method ◽  
Internal Consistency ◽  
Data Sets ◽  
Independent Data ◽  
Simple Method ◽  
X Ray ◽  
Area Detector

A simple numerical method has been developed to correct for absorption and decay effects in the intensities measured by area-detector X-ray diffractometers. Application of this method improves not only the internal consistency of symmetry-equivalent reflections, but also the agreement between the two independent data sets.

scholarly journals Assessment of Waveform Similarity in Clinical Gait Data: The Linear Fit Method

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
M. Iosa ◽  
A. Cereatti ◽  
A. Merlo ◽  
I. Campanini ◽  
S. Paolucci ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Real Data ◽  
Gait Pattern ◽  
Data Sets ◽  
Good Reliability ◽  
Simple Method ◽  
Reliability Sensitivity ◽  
Waveform Similarity ◽  
Linear Fit ◽  
Clinical Gait Analysis

The assessment of waveform similarity is a crucial issue in gait analysis for the comparison of kinematic or kinetic patterns with reference data. A typical scenario is in fact the comparison of a patient’s gait pattern with a relevant physiological pattern. This study aims to propose and validate a simple method for the assessment of waveform similarity in terms of shape, amplitude, and offset. The method relies on the interpretation of these three parameters, obtained through a linear fit applied to the two data sets under comparison plotted one against the other after time normalization. The validity of this linear fit method was tested in terms of appropriateness (comparing real gait data of 34 patients with cerebrovascular accident with those of 15 healthy subjects), reliability, sensitivity, and specificity (applying a cluster analysis on the real data). Results showed for this method good appropriateness, 94.1% of sensitivity, 93.3% of specificity, and good reliability. The LFM resulted in a simple method suitable for analysing the waveform similarity in clinical gait analysis.

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