Диагностика заболеваний сердечно-сосудистой системы и ишемии миокарда с помощью магниторезонансной визуализации с контрастным усилением

Показана возможность выявления патологических изменений в миокарде (инфаркт, ишемия) и сосудистой системе (стеноз) с помощью магниторезонансной визуализации с контрастным усилением.The possibility to reveal pathological changes in the myocardium (infarction, ischemia) and vascular system (stenosis) using magnetic resonance imaging with contrast enhancement is shown

On the sample size dependence of the critical current density in MgB2_2 superconductors

Sample size dependent critical current density has been observed in magnesium diboride superconductors. At high fields, larger samples provide higher critical current densities, while at low fields, larger samples give rise to lower critical current densities. The explanation for this surprising result is proposed in this study based on the electric field generated in the superconductors. The dependence of the current density on the sample size has been derived as a power law $j\propto R^{1/n}$ ($n$ is the $n$ factor characterizing $E-j$ curve $E=E_c(j/j_c)^n$). This dependence provides one with a new method to derive the $n$ factor and can also be used to determine the dependence of the activation energy on the current density.Comment: Revtex, 4 pages, 5 figure

Brain Plasticity and Intellectual Ability Are Influenced by Shared Genes

Although the adult brain is considered to be fully developed and stable until senescence when its size steadily decreases, such stability seems at odds with continued human (intellectual) development throughout life. Moreover, although variation in human brain size is highly heritable, we do not know the extent to which genes contribute to individual differences in brain plasticity. In this longitudinal magnetic resonance imaging study in twins, we report considerable thinning of the frontal cortex and thickening of the medial temporal cortex with increasing age and find this change to be heritable and partly related to cognitive ability. Specifically, adults with higher intelligence show attenuated cortical thinning and more pronounced cortical thickening over time than do subjects with average or below average IQ. Genes influencing variability in both intelligence and brain plasticity partly drive these associations. Thus, not only does the brain continue to change well into adulthood, these changes are functionally relevant because they are related to intelligence. Copyright©2010 the authors

Is synthetic hexaploid wheat a useful germplasm source for increasing grain size and yield in bread wheat breeding?

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Гераклиты – карбонатные образования газовых источников и грязевых вулканов миоцена

Морфологические признаки, минералогия, геохимия и состав газовой фракции гераклитов подтвердили их генетическую общность с современными карбонатными образованиями метановых источников и грязевых вулканов Черного моря. Присутствие в гераклитах битумов, метана и этана может служить индикатором наличия нефти и газа на Гераклейском полуострове и прилегающему к нему шельфе.Морфологічні ознаки, мінералогія, геох!мія i склад газової фракцїї гераклитів підтвердили їх генетичну спільність із сучасними карбонатними утвореннями метанових джерел i грязьових вулканів Чорного моря. Присутність у гераклітах бітумів, метану i етану може слугувати як індикатор наявності нафти й газу на Гераклейськом niвocтpoвi i прилеглому до нього шельфі.Morphological attributes, mineralogy, geochemistry and structure of gas fraction Geraklit have confirmed their proximity to modern carbonate formations of methane sources and mud volcanoes in the Black sea. Presence of bitumen, methane and ethane in Geraklites serves as indicator of occurence of oil fields and gas on the Geraklejskij peninsula and adjoining to them a shelf

Contributing factors to advanced brain aging in depression and anxiety disorders

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d=0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d=0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b=4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.Education and Child Studie

Genetic contributions to human brain morphology and intelligence

Variation in gray matter (GM) and white matter (WM) volume of the adult human brain is primarily genetically determined. Moreover, total brain volume is positively correlated with general intelligence, and both share a common genetic origin. However, although genetic effects on morphology of specific GM areas in the brain have been studied, the heritability of focal WM is unknown. Similarly, it is unresolved whether there is a common genetic origin of focal GM and WM structures with intelligence. We explored the genetic influence on focal GM and WM densities in magnetic resonance brain images of 54 monozygotic and 58 dizygotic twin pairs and 34 of their siblings. For genetic analyses, we used structural equation modeling and voxel-based morphometry. To explore the common genetic origin of focal GM and WM areas with intelligence, we obtained cross-trait/cross-twin correlations in which the focal GM and WM densities of each twin are correlated with the psychometric intelligence quotient of his/her cotwin. Genes influenced individual differences in left and right superior occipitofrontal fascicle (heritability up to 0.79 and 0.77), corpus callosum (0.82, 0.80), optic radiation (0.69, 0.79), corticospinal tract (0.78, 0.79), medial frontal cortex (0.78, 0.83), superior frontal cortex (0.76, 0.80), superior temporal cortex (0.80, 0.77), left occipital cortex (0.85), left postcentral cortex (0.83), left posterior cingulate cortex (0.83), right parahippocampal cortex (0.69), and amygdala (0.80, 0.55). Intelligence shared a common genetic origin with superior occipitofrontal, callosal, and left optical radiation WM and frontal, occipital, and parahippocampal GM (phenotypic correlations up to 0.35). These findings point to a neural network that shares a common genetic origin with human intelligence

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Do we measure gray matter activation with functional diffusion tensor imaging?

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