Environmental, personal, and genetic determinants of response to vitamin D supplementation in older adults

CONTEXT AND OBJECTIVE: Suboptimal vitamin D status can be corrected by vitamin D supplementation, but individual responses to supplementation vary. We aimed to examine genetic and nongenetic determinants of change in serum 25-hydroxyvitamin D (25(OH)D) after supplementation. DESIGN AND PARTICIPANTS: We used data from a pilot randomized controlled trial in which 644 adults aged 60 to 84 years were randomly assigned to monthly doses of placebo, 30 000 IU, or 60 000 IU vitamin D3 for 12 months. Baseline characteristics were obtained from a self-administered questionnaire. Eighty-eight single-nucleotide polymorphisms (SNPs) in 41 candidate genes were genotyped using Sequenom MassArray technology. Serum 25(OH)D levels before and after the intervention were measured using the Diasorin Liaison platform immunoassay. We used linear regression models to examine associations between genetic and nongenetic factors and change in serum 25(OH)D levels. RESULTS: Supplement dose and baseline 25(OH)D level explained 24% of the variability in response to supplementation. Body mass index, self-reported health status, and ambient UV radiation made a small additional contribution. SNPs in CYP2R1, IRF4, MC1R, CYP27B1, VDR, TYRP1, MCM6, and HERC2 were associated with change in 25(OH)D level, although only CYP2R1 was significant after adjustment for multiple testing. Models including SNPs explained a similar proportion of variability in response to supplementation as models that included personal and environmental factors. CONCLUSION: Stepwise regression analyses suggest that genetic variability may be associated with response to supplementation, perhaps suggesting that some people might need higher doses to reach optimal 25(OH)D levels or that there is variability in the physiologically normal level of 25(OH)D.

Accelerating junior-secondary mathematics

Unfortunately, in Australia there is a prevalence of mathematically underperforming junior-secondary students in low-socioeconomic status schools. This requires targeted intervention to develop the affected students’ requisite understanding in preparation for post-compulsory study and employment and, ultimately, to increase their life chances. To address this, the ongoing action research project presented in this paper is developing a curriculum of accelerated learning, informed by a lineage of cognitivist-based structural sequence theory building activity (e.g., Cooper & Warren, 2011). The project’s conceptual framework features three pillars: the vertically structured sequencing of concepts; pedagogy grounded in students’ reality and culture; and professional learning to support teachers’ implementation of the curriculum (Cooper, Nutchey, & Grant, 2013). Quantitative and qualitative data informs the ongoing refinement of the theory, the curriculum, and the teacher support.

Structuring representations of continuous data through investigations in grade 1

The study of data modelling with elementary students involves the analysis of a developmental process beginning with children’s investigations of meaningful contexts: visualising, structuring, and representing data and displaying data in simple graphs (English, 2012; Lehrer & Schauble, 2005; Makar, Bakker, & Ben-Zvi, 2011). A 3-year longitudinal study investigated young children’s data modelling, integrating mathematical and scientific investigations. One aspect of this study involved a researcher-led teaching experiment with 21 mathematically able Grade 1 students. The study aimed to describe explicit developmental features of students’ representations of continuous data...

Conformational Spread as a Mechanism for Cooperativity in the Bacterial Flagellar Switch

The bacterial flagellar switch that controls the direction of flagellar rotation during Chemotaxis has a highly cooperative response. This has previously been understood in terms of the classic two-state, concerted model of allosteric regulation. Here, we used high-resolution optical microscopy to observe switching of single motors and uncover the stochastic multistate nature of the switch. Our observations are in detailed quantitative agreement with a recent general model of allosteric cooperativity that exhibits conformational spread-the stochastic growth and shrinkage of domains of adjacent subunits sharing a particular conformational state. We expect that conformational spread will be important in explaining cooperativity in other large signaling complexes.

Journalism students' motivations and expectations of their work in comparative perspective

Based on a survey of 4,393 journalism students in Australia, Brazil, Chile, Mexico, South Africa, Spain, Switzerland, and the United States, this study provides much-needed comparative evidence about students’ motivations for becoming journalists, their future job plans, and expectations. Findings show not only an almost universal decline in students’ desire to work in journalism by the end of their program but also important national differences in terms of the journalistic fields in which they want to work, as well as their job expectations. The results reinforce the need to take into account national contexts when examining journalism education across the globe.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Using conservation evidence to guide management

Almost 10 years ago, Pullin and Knight (2001) called for an “effectiveness revolution in conservation” to be enabled by the systematic evaluation of evidence for conservation decision making. Drawing from the model used in clinicalmedicine, they outlined the concept of “evidencebased conservation” in which existing information, or evidence, from relevant and rigorous research is compiled and analyzed in a systematic manner to inform conservation actions (Cochrane 1972). The promise of evidencebased conservation has generated significant interest; 25 systematic reviews have been completed since 2004 and dozens are underway (Collaboration for Environmental Evidence 2010). However we argue that an “effectiveness revolution” (Pullin & Knight 2001) in conservation will not be possible unless mechanisms are devised for incorporating the growing evidence base into decision frameworks. For conservation professionals to accomplish the missions of their organizations they must demonstrate that their actions actually achieve objectives (Pullin & Knight 2009). Systematic evaluation provides a framework for objectively evaluating the effectiveness of actions. To leverage the benefit of these evaluations, we need resource-allocation systems that are responsive to their outcomes. The allocation of conservation resources is often the product of institutional priorities or reliance on intuition (Sutherland et al. 2004; Pullin & Knight 2005; Cook et al. 2010). We highlight the NICE technologyappraisal process because it provides an example of formal integration of systematic-evidence evaluation with provision of guidance for action. The transparent process, which clearly delineates costs and benefits of each alternative action, could also provide the public with new insight into the environmental effects of different decisions. This insight could stimulate a wider discussion about investment in conservation by demonstrating how changes in funding might affect the probability of achieving conservation objectives. ©2010 Society for Conservation Biology

Genetic risk score analysis indicates migraine with and without comorbid depression are genetically different disorders

Migraine and major depressive disorder (MDD) are comorbid, moderately heritable and to some extent influenced by the same genes. In a previous paper, we suggested the possibility of causality (one trait causing the other) underlying this comorbidity. We present a new application of polygenic (genetic risk) score analysis to investigate the mechanisms underlying the genetic overlap of migraine and MDD. Genetic risk scores were constructed based on data from two discovery samples in which genome-wide association analyses (GWA) were performed for migraine and MDD, respectively. The Australian Twin Migraine GWA study (N = 6,350) included 2,825 migraine cases and 3,525 controls, 805 of whom met the diagnostic criteria for MDD. The RADIANT GWA study (N = 3,230) included 1,636 MDD cases and 1,594 controls. Genetic risk scores for migraine and for MDD were used to predict pure and comorbid forms of migraine and MDD in an independent Dutch target sample (NTR-NESDA, N = 2,966), which included 1,476 MDD cases and 1,058 migraine cases (723 of these individuals had both disorders concurrently). The observed patterns of prediction suggest that the 'pure' forms of migraine and MDD are genetically distinct disorders. The subgroup of individuals with comorbid MDD and migraine were genetically most similar to MDD patients. These results indicate that in at least a subset of migraine patients with MDD, migraine may be a symptom or consequence of MDD. © 2013 Springer-Verlag Berlin Heidelberg.

Pituitary volume and early treatment response in drug-naïve first-episode psychosis patients

BACKGROUND: An early response to antipsychotic treatment in patients with psychosis has been associated with a better course and outcome. However, factors that predict treatment response are not well understood. The onset of schizophrenia and related disorders has been associated with increased levels of stress and hyper-activation of the hypothalamic-pituitary-adrenal (HPA) axis. This study examined whether pituitary volume at the onset of psychosis may be a potential predictor of early treatment response in first-episode psychosis (FEP) patients. METHODS: We investigated the relationship between baseline pituitary volume and symptomatic treatment response over 12 weeks using mixed model analysis in a sample of 42 drug-naïve or early treated FEP patients who participated in a controlled dose-finding study of quetiapine fumarate. Logistic regression was used to examine predictors of treatment response. Pituitary volume was measured from magnetic resonance imaging scans that were obtained upon entry into the trial. RESULTS: Larger pituitary volume was associated with less improvement in overall psychotic symptoms (Brief Psychiatric Rating Scale (BPRS) P=0.031) and positive symptoms (BPRS positive symptom subscale P=0.010). Regardless of gender, patients with a pituitary volume at the 25th percentile (413 mm(3)) were approximately three times more likely to respond to treatment by week 12 than those at the 75th percentile (635 mm(3)) (odds ratio=3.07, CI: 0.90-10.48). CONCLUSION: The association of baseline pituitary volumes with early treatment response highlights the importance of the HPA axis in emerging psychosis. Potential implications for treatment strategies in early psychosis are discussed.

EVpedia: A community web portal for extracellular vesicles research

Motivation Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging. Results We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research. Availability and implementation The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Combining meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.

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.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.