A practical guide to terminology for kimberlite facies: A systematic progression from descriptive to genetic, including a pocket guide

Kimberlite terminology remains problematic because both descriptive and genetic terms are mixed together in most existing terminology schemes. In addition, many terms used in existing kimberlite terminology schemes are not used in mainstream volcanology, even though kimberlite bodies are commonly the remains of kimberlite volcanic vents and edifices. We build on our own recently published approach to kimberlite facies terminology, involving a systematic progression from descriptive to genetic. The scheme can be used for both coherent kimberlite (i.e. kimberlite that was emplaced without undergoing any fragmentation processes and therefore preserving coherent igneous textures) and fragmental kimberlites. The approach involves documentation of components, textures and assessing the degree and effects of alteration on both components and original emplacement textures. This allows a purely descriptive composite component, textural and compositional petrological rock or deposit name to be constructed first, free of any biases about emplacement setting and processes. Then important facies features such as depositional structures, contact relationships and setting are assessed, leading to a composite descriptive and genetic name for the facies or rock unit that summarises key descriptive characteristics, emplacement processes and setting. Flow charts summarising the key steps in developing a progressive descriptive to genetic terminology are provided for both coherent and fragmental facies/deposits/rock units. These can be copied and used in the field, or in conjunction with field (e.g. drill core observations) and petrographic data. Because the approach depends heavily on field scale observations, characteristics and process interpretations, only the first descriptive part is appropriate where only petrographic observations are being made. Where field scale observations are available the progression from developing descriptive to interpretative terminology can be used, especially where some petrographic data also becomes available.

A new approach to kimberlite facies terminology using a revised general approach to the nomenclature of all volcanic rocks and deposits: Descriptive to genetic

Although kimberlite pipes/bodies are usually the remains of volcanic vents, in-vent deposits, and subvolcanic intrusions, the terminology used for kimberlite rocks has largely developed independently of that used in mainstream volcanology. Existing kimberlite terminology is not descriptive and includes terms that are rarely used, used differently, and even not used at all in mainstream volcanology. In addition, kimberlite bodies are altered to varying degrees, making application of genetic terminology difficult because original components and depositional textures are commonly masked by alteration. This paper recommends an approach to the terminology for kimberlite rocks that is consistent with usage for other volcanic successions. In modern terrains the eruption and emplacement origins of deposits can often be readily deduced, but this is often not the case for old, variably altered and deformed rock successions. A staged approach is required whereby descriptive terminology is developed first, followed by application of genetic terminology once all features, including the effects of alteration on original texture and depositional features, together with contact relationships and setting, have been evaluated. Because many volcanic successions consist of both primary volcanic deposits as well as volcanic sediments, terminology must account for both possibilities.

The empire of cancer: Gene patents and cancer voices

In his book, The Emperor of All Maladies, Siddhartha Mukherjee writes a history of cancer — "It is a chronicle of an ancient disease — once a clandestine, 'whispered-about' illness — that has metamorphosed into a lethal shape-shifting entity imbued with such penetrating metaphorical, medical, scientific, and political potency that cancer is often described as the defining plague of our generation." Increasingly, an important theme in the history of cancer is the role of law, particularly in the field of intellectual property law. It is striking that a number of contemporary policy debates over intellectual property and public health have concerned cancer research, diagnosis, and treatment. In the area of access to essential medicines, there has been much debate over Novartis’ patent application in respect of Glivec, a treatment for leukaemia. India’s Supreme Court held that the Swiss company’s patent application violated a safeguard provision in India’s patent law designed to stop evergreening. In the field of tobacco control, the Australian Government introduced plain packaging for tobacco products in order to address the health burdens associated with the tobacco epidemic. This regime was successfully defended in the High Court of Australia. In the area of intellectual property and biotechnology, there have been significant disputes over the Utah biotechnology company Myriad Genetics and its patents in respect of genetic testing for BRCA1 and BRCA2, which are related to breast cancer and ovarian cancer. The Federal Court of Australia handed down a decision on the validity of Myriad Genetics’ patent in respect of genetic testing for BRCA1 in February 2013. The Supreme Court of the United States heard a challenge to the validity of Myriad Genetics’ patents in this area in April 2013, and handed down a judgment in July 2013. Such disputes have involved tensions between intellectual property rights, and public health. This article focuses upon one of these important test cases involving intellectual property, public health, and cancer research. In June 2010, Cancer Voices Australia and Yvonne D’Arcy brought an action in the Federal Court of Australia against the validity of a BRCA1 patent — held by Myriad Genetics Inc, the Centre de Recherche du Chul, the Cancer Institute of Japan and Genetic Technologies Limited. Yvonne D’Arcy — a Brisbane woman who has had treatment for breast cancer — maintained: "I believe that what they are doing is morally and ethically corrupt and that big companies should not control any parts of the human body." She observed: "For my daughter, I've had her have [sic] mammograms, etc, because of me but I would still like her to be able to have the test to see if the mutation gene is in there from me." The applicants made the following arguments: "Genes and the information represented by human gene sequences are products of nature universally present in each individual, and the information content of a human gene sequence is fixed. Genetic variations or mutations are products of nature. The isolation of the BRCA1 gene mutation from the human body constitutes no more than a medical or scientific discovery of a naturally occurring phenomenon and does not give rise to a patentable invention." The applicants also argued that "the alleged invention is not a patentable invention in that, so far as claimed in claims 1–3, it is not a manner of manufacture within the meaning of s 6 of the Statute of Monopolies". The applicants suggested that "the alleged invention is a mere discovery". Moreover, the applicants contended that "the alleged invention of each of claims 1-3 is not a patentable invention because they are claims for biological processes for the generation of human beings". The applicants, though, later dropped the argument that the patent claims related to biological processes for the generation of human beings. In February 2013, Nicholas J of the Federal Court of Australia considered the case brought by Cancer Voices Australia and Yvonne D’Arcy against Myriad Genetics. The judge presented the issues in the case, as follows: "The issue that arises in this case is of considerable importance. It relates to the patentability of genes, or gene sequences, and the practice of 'gene patenting'. Briefly stated, the issue to be decided is whether under the Patents Act 1990 (Cth) a valid patent may be granted for a claim that covers naturally occurring nucleic acid — either deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) — that has been 'isolated'". In this context, the word "isolated" implies that naturally occurring nucleic acid found in the cells of the human body, whether it be DNA or RNA, has been removed from the cellular environment in which it naturally exists and separated from other cellular components also found there. The genes found in the human body are made of nucleic acid. The particular gene with which the patent in suit is concerned (BRCA1) is a human breast and ovarian cancer disposing gene. Various mutations that may be present in this gene have been linked to various forms of cancer including breast cancer and ovarian cancer.' The judge held in this particular case that Myriad Genetics’ patent claims were a "manner of manufacture" under s 6 of the Statute of Monopolies and s 18(1)(a) of the Patents Act 1990 (Cth). The matter is currently under appeal in the Full Court of the Federal Court of Australia. This article interprets the dispute over Myriad Genetics in light of the scholarly work of Nobel Laureate Professor Joseph Stiglitz on inequality. Such work has significant explanatory power in the context of intellectual property and biotechnology. First, Stiglitz has contended that "societal inequality was a result not just of the laws of economics, but also of how we shape the economy — through politics, including through almost every aspect of our legal system". Stiglitz is concerned that "our intellectual property regime … contributes needlessly to the gravest form of inequality." He maintains: "The right to life should not be contingent on the ability to pay." Second, Stiglitz worries that "some of the most iniquitous aspects of inequality creation within our economic system are a result of 'rent-seeking': profits, and inequality, generated by manipulating social or political conditions to get a larger share of the economic pie, rather than increasing the size of that pie". He observes that "the most iniquitous aspect of this wealth appropriation arises when the wealth that goes to the top comes at the expense of the bottom." Third, Stiglitz comments: "When the legal regime governing intellectual property rights is designed poorly, it facilitates rent-seeking" and "the result is that there is actually less innovation and more inequality." He is concerned that intellectual property regimes "create monopoly rents that impede access to health both create inequality and hamper growth more generally." Finally, Stiglitz has recommended: "Government-financed research, foundations, and the prize system … are alternatives, with major advantages, and without the inequality-increasing disadvantages of the current intellectual property rights system.’" This article provides a critical analysis of the Australian litigation and debate surrounding Myriad Genetics’ patents in respect of genetic testing for BRCA1. First, it considers the ruling of Nicholas J in the Federal Court of Australia that Myriad Genetics’ patent was a manner of manufacture as it related to an artificially created state of affairs, and not mere products of nature. Second, it examines the policy debate over gene patents in Australia, and its relevance to the litigation involving Myriad Genetics. Third, it examines comparative law, and contrasts the ruling by Nicholas J in the Federal Court of Australia with developments in the United States, Canada, and the European Union. Fourth, this piece considers the reaction to the decision of Nicholas at first instance in Australia. Fifth, the article assesses the prospects of an appeal to the Full Federal Court of Australia over the Myriad Genetics’ patents. Finally, this article observes that, whatever happens in respect of litigation against Myriad Genetics, there remains controversy over Genetic Technologies Limited. The Melbourne firm has been aggressively licensing and enforcing its related patents on non-coding DNA and genomic mapping.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Genetic effects on the cerebellar role in working memory: Same brain, different genes?

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n= 430; aged 16-30. years), that the cerebellum is strongly, and reliably (n=30 rescans), activated during an n-back working memory task, particularly lobules I-IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a 2 = 0.55, p = 0.04, left; a 2 = 0.74, p = 0.006, right), bilateral parietal (a 2 = 0.85, p < 0.001, left; a 2 = 0.84, p < 0.001, right), and left occipital (a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto- occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.

Quantitative genetic modeling of lateral ventricular shape and volume using multi-atlas fluid image alignment in twins

Despite substantial progress in measuring the 3D profile of anatomical variations in the human brain, their genetic and environmental causes remain enigmatic. We developed an automated system to identify and map genetic and environmental effects on brain structure in large brain MRI databases . We applied our multi-template segmentation approach ("Multi-Atlas Fluid Image Alignment") to fluidly propagate hand-labeled parameterized surface meshes into 116 scans of twins (60 identical, 56 fraternal), labeling the lateral ventricles. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps revealed 3D heritability patterns, and their significance, with and without adjustments for global brain scale. These maps visualized detailed profiles of environmental versus genetic influences on the brain, extending genetic models to spatially detailed, automatically computed, 3D maps.

Mapping genetic influences on ventricular structure in twins

Despite substantial progress in measuring the anatomical and functional variability of the human brain, little is known about the genetic and environmental causes of these variations. Here we developed an automated system to visualize genetic and environmental effects on brain structure in large brain MRI databases. We applied our multi-template segmentation approach termed "Multi-Atlas Fluid Image Alignment" to fluidly propagate hand-labeled parameterized surface meshes, labeling the lateral ventricles, in 3D volumetric MRI scans of 76 identical (monozygotic, MZ) twins (38 pairs; mean age = 24.6 (SD = 1.7)); and 56 same-sex fraternal (dizygotic, DZ) twins (28 pairs; mean age = 23.0 (SD = 1.8)), scanned as part of a 5-year research study that will eventually study over 1000 subjects. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps, derived from path analysis, revealed patterns of heritability, and their significance, in 3D. Path coefficients for the 'ACE' model that best fitted the data indicated significant contributions from genetic factors (A = 7.3%), common environment (C = 38.9%) and unique environment (E = 53.8%) to lateral ventricular volume. Earlier-maturing occipital horn regions may also be more genetically influenced than later-maturing frontal regions. Maps visualized spatially-varying profiles of environmental versus genetic influences. The approach shows promise for automatically measuring gene-environment effects in large image databases.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.