A Theory for High-TcT_c Superconductors Considering Inhomogeneous Charge Distribution

We propose a general theory for the critical $T_c$ and pseudogap $T^*$ temperature dependence on the doping concentration for high-$T_c$ oxides, taking into account the charge inhomogeneities in the $CuO_2$ planes. The well measured experimental inhomogeneous charge density in a given compound is assumed to produce a spatial distribution of local $\rho(r)$. These differences in the local charge concentration is assumed to yield insulator and metallic regions, possibly in a stripe morphology. In the metallic region, the inhomogeneous charge density yields also spatial distributions of superconducting critical temperatures $T_c(r)$ and zero temperature gap $\Delta_0(r)$. For a given sample, the measured onset of vanishing gap temperature is identified as the pseudogap temperature, that is, $T^*$, which is the maximum of all $T_c(r)$. Below $T^*$, due to the distribution of $T_c(r)$'s, there are some superconducting regions surrounded by insulator or metallic medium. The transition to a superconducting state corresponds to the percolation threshold among the superconducting regions with different $T_c(r)$'s. To model the charge inhomogeneities we use a double branched Poisson-Gaussian distribution. To make definite calculations and compare with the experimental results, we derive phase diagrams for the BSCO, LSCO and YBCO families, with a mean field theory for superconductivity using an extended Hubbard Hamiltonian. We show also that this novel approach provides new insights on several experimental features of high-$T_c$ oxides.Comment: 7 pages, 5 eps figures, corrected typo

Colossal dielectric constants in transition-metal oxides

Many transition-metal oxides show very large ("colossal") magnitudes of the dielectric constant and thus have immense potential for applications in modern microelectronics and for the development of new capacitance-based energy-storage devices. In the present work, we thoroughly discuss the mechanisms that can lead to colossal values of the dielectric constant, especially emphasising effects generated by external and internal interfaces, including electronic phase separation. In addition, we provide a detailed overview and discussion of the dielectric properties of CaCu3Ti4O12 and related systems, which is today's most investigated material with colossal dielectric constant. Also a variety of further transition-metal oxides with large dielectric constants are treated in detail, among them the system La2-xSrxNiO4 where electronic phase separation may play a role in the generation of a colossal dielectric constant.Comment: 31 pages, 18 figures, submitted to Eur. Phys. J. for publication in the Special Topics volume "Cooperative Phenomena in Solids: Metal-Insulator Transitions and Ordering of Microscopic Degrees of Freedom

Type I and II endometrial cancers: have they different risk factors?

PurposeEndometrial cancers have long been divided into estrogen-dependent type I and the less common clinically aggressive estrogen-independent type II. Little is known about risk factors for type II tumors because most studies lack sufficient cases to study these much less common tumors separately. We examined whether so-called classical endometrial cancer risk factors also influence the risk of type II tumors.Patients and MethodsIndividual-level data from 10 cohort and 14 case-control studies from the Epidemiology of Endometrial Cancer Consortium were pooled. A total of 14,069 endometrial cancer cases and 35,312 controls were included. We classified endometrioid (n = 7,246), adenocarcinoma not otherwise specified (n = 4,830), and adenocarcinoma with squamous differentiation (n = 777) as type I tumors and serous (n = 508) and mixed cell (n = 346) as type II tumors.ResultsParity, oral contraceptive use, cigarette smoking, age at menarche, and diabetes were associated with type I and type II tumors to similar extents. Body mass index, however, had a greater effect on type I tumors than on type II tumors: odds ratio (OR) per 2 kg/m(2) increase was 1.20 (95% CI, 1.19 to 1.21) for type I and 1.12 (95% CI, 1.09 to 1.14) for type II tumors (P-heterogeneity < .0001). Risk factor patterns for high-grade endometrioid tumors and type II tumors were similar.ConclusionThe results of this pooled analysis suggest that the two endometrial cancer types share many common etiologic factors. The etiology of type II tumors may, therefore, not be completely estrogen independent, as previously believed. (C) 2013 by American Society of Clinical Oncolog

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Superficially Located White Matter Structures Commonly Seen in the Human and the Macaque Brain with Diffusion Tensor Imaging

The white matter of the brain consists of fiber tracts that connect different regions of the brain. Among these tracts, the intrahemispheric cortico-cortical connections are called association fibers. The U-fibers are short association fibers that connect adjacent gyri. These fibers were thought to work as part of the cortico-cortical networks to execute associative brain functions. However, their anatomy and functions have not been documented in detail for the human brain. In past studies, U-fibers have been characterized in the human brain with diffusion tensor imaging (DTI). However, the validity of such findings remains unclear. In this study, DTI of the macaque brain was performed, and the anatomy of U-fibers was compared with that of the human brain reported in a previous study. The macaque brain was chosen because it is the most commonly used animal model for exploring cognitive functions and the U-fibers of the macaque brain have been already identified by axonal tracing studies, which makes it an ideal system for confirming the DTI findings. Ten U-fibers found in the macaque brain were also identified in the human brain, with a similar organization and topology. The delineation of these species-conserved white matter structures may provide new options for understanding brain anatomy and function