A vibrational spectroscopic study of tengerite-(Y) Y2(CO3)3 2–3H2O

The mineral tengerite-(Y) has been studied by vibrational spectroscopy. Multiple carbonate stretching modes are observed and support the concept of non-equivalent carbonate units in the tengerite-(Y) structure. Intense sharp bands at 464, 479 and 508 cm−1 are assigned to YO stretching modes. Raman bands at 765 and 775 cm−1 are assigned to the CO32− ν4 bending modes and Raman bands at 589, 611, 674 and 689 cm−1 are assigned to the CO32− ν2 bending modes. Multiple Raman and infrared bands in the OH stretching region are observed, proving the existence of water in different molecular environments in the structure of tengerite-(Y).

Raman and infrared spectroscopic study of kamphaugite-(Y)

We have studied the carbonate mineral kamphaugite-(Y)(CaY(CO3)2(OH)·H2O), a mineral which contains yttrium and specific rare earth elements. Chemical analysis shows the presence of Ca, Y and C. Back scattering SEM appears to indicate a single pure phase. The vibrational spectroscopy of kamphaugite-(Y) was obtained using a combination of Raman and infrared spectroscopy. Two distinct Raman bands observed at 1078 and 1088cm(-1) provide evidence for the non-equivalence of the carbonate anion in the kamphaugite-(Y) structure. Such a concept is supported by the number of bands assigned to the carbonate antisymmetric stretching mode. Multiple bands in the ν4 region offers further support for the non-equivalence of carbonate anions in the structure. Vibrational spectroscopy enables aspects of the structure of the mineral kamphaugite-(Y) to be assessed.

Scanning electron microscopy with energy dispersive spectroscopy and Raman and infrared spectroscopic study of tilleyite Ca5Si2O7(CO3)2-Y

The mineral tilleyite-Y, a carbonate-silicate of calcium, has been studied by scanning electron microscopy with chemical analysis using energy dispersive spectroscopy (EDX) and Raman and infrared spectroscopy. Multiple carbonate stretching modes are observed and support the concept of non-equivalent carbonate units in the tilleyite structure. Multiple Raman and infrared bands in the OH stretching region are observed, proving the existence of water in different molecular environments in the structure of tilleyite. Vibrational spectroscopy offers new information on the mineral tilleyite.

The Lazarus effect: The (RED) campaign, and creative capitalism

"4,400 people die every day of AIDS in sub-Saharan Africa. Treatment exists. In about 60 days, a patient can go from here to here. We call this transformation the Lazarus Effect. It is the result of two pills a day taken by a HIV/AIDS patient for about 60 days. Learn more about how you can help give people the chance of life and joinred.com."The Lazarus Effect video, the (RED) Campaign.This Chapter explores how a number of non-government organizations, charities, and philanthropists have promoted ’grants' as a means of stimulating investment in research and development into neglected diseases. Each section considers the nature of the campaign; the use of intellectual property rights, such as trade marks; and the criticisms made of such endeavors. Section 2 looks at the (RED) Campaign, which is designed to boost corporate funding and consumer support for the Global Fund. Section 3 examines the role of the Gates Foundation in funding research and development in respect of infectious diseases. It explores the championing by Bill Gates of ’creative capitalism'. Section 4 considers the part of the Clinton Foundation in the debate over access to essential medicines. The Chapter concludes that, despite their qualities, such marketing initiatives fail to address the underlying inequalities and injustices of international patent law.

Contribución de la fuerza y el dolor en la función del paciente con artrosis trapecio metacarpiana. Estudio transversal

Objetivo Establecer una posible relación entre la fuerza (Jamar), el dolor (EVA) y la capacidad funcional referida por el paciente (DASH) determinando en qué grado influyen unas en otras. Estudio observacional transversal analítico. Participantes Muestra de 72 pacientes que presentaban una artrosis trapecio metacarpiana grado 2-3 de Eaton. Los pacientes fueron reclutados cuando acudían a la Unidad de Cirugía de mano. Método Se realizaron mediciones de fuerza de agarre, pinza, valoración del dolor y funcionalidad, y se establecieron las correlaciones entre cada una de ellas. Resultados El modelo más significativo para la función (R2 =0.83) incluye la variable dolor y la fuerza. Pero es la fuerza punta contra punta la que presenta una mayor correlación con el cuestionario DASH (B-estandarizado: –57). Respecto al dolor, influye en todas las mediciones de fuerza realizadas con el dinamómetro, siendo también la fuerza de la pinza punta contra punta la que presenta una mayor correlación. Conclusiones Los hallazgos corroboran que existe una correlación significativa entre la función referida por el paciente y variables que podemos medir en consulta, como la fuerza del puño y la pinza. Pero también esta correlación es significativa entre las variables función y dolor entre sí, pero es la pinza punta contra punta la que presenta una mayor asociación con el cuestionario DASH. Abstract in English Objective To assess the relationship between muscle strength (Jama), and pain (VAS) levels with hand function (DASH) in patients with trapeziometarcapal osteoarthritis. Cross-sectional study. Participants Sample of 72 patients with osteoarthritis stage 2-3 (Eaton) and trapeziometacarpal osteoarthritis. Patients were recruited when they came to the Hand Surgery Unit. Method Grip strength, pinch, pain and hand function were measured, and correlation and regression coefficients between them were obtained. Results For function, the most significant model (R2 = 0.83) included pain and strength. But it is tip to tip pinch force which has a stronger relationship with DASH (Standardized B: –57) questionnaire. Pain also influenced strength measured with the dynamometer but it was tip to tip pinch force that was the most affected. Conclusions Findings confirm that there is a significant correlation between function referred by the patient and variables that can be measured in the clinic such as grip strength and pinch. The correlation between pain intensity and function was also significant, but tip to tip pinch strength had the greatest impact on the function.

A lagrangian formulation for statistical fluid registration

We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.

Mapping the regional influence of genetics on brain structure variability - A Tensor-Based Morphometry study

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

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.

Improving fluid registration through white matter segmentation in a twin study design

Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.

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.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.

Reducing structural variation to determine the genetics of white matter integrity across hemispheres - a dti study of 100 twins

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

Genetics of path lengths in brain connectivity networks: HARDI-based maps in 457 adults

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.