An electron channeling study of polycrystalline YBa2Cu3Ox

An electron channeling study has been done on large grained YBa2Cu3Ox samples. Selected area channeling patterns (SACP) were used to examine several dozen grains on electropolished surfaces and it was demonstrated that (a) the twin planes observed in polarized optical light microscopy lie parallel to {110} crystal planes, and (b) the long flat sides of high aspect ratio grains are formed by basal planes, and the shorter sides are formed by either (010), (100), or {110} planes. A majority of the large grains examined were found to contain subgrains, misaligned by 0.5°–1° and ranging in size from less than 3 to 20 μm. The origin of the subgrains is not understood

Bayesian mixed-effects inference on classification performance in hierarchical data sets

Classification algorithms are frequently used on data with a natural hierarchical structure. For instance, classifiers are often trained and tested on trial-wise measurements, separately for each subject within a group. One important question is how classification outcomes observed in individual subjects can be generalized to the population from which the group was sampled. To address this question, this paper introduces novel statistical models that are guided by three desiderata. First, all models explicitly respect the hierarchical nature of the data, that is, they are mixed-effects models that simultaneously account for within-subjects (fixed-effects) and across-subjects (random-effects) variance components. Second, maximum-likelihood estimation is replaced by full Bayesian inference in order to enable natural regularization of the estimation problem and to afford conclusions in terms of posterior probability statements. Third, inference on classification accuracy is complemented by inference on the balanced accuracy, which avoids inflated accuracy estimates for imbalanced data sets. We introduce hierarchical models that satisfy these criteria and demonstrate their advantages over conventional methods usingMCMC implementations for model inversion and model selection on both synthetic and empirical data. We envisage that our approach will improve the sensitivity and validity of statistical inference in future hierarchical classification studies. © 2012

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

The Residual Stress Relaxation Behavior of Weldments During Cyclic Loading

Accurate measurement of residual stress is necessary to obtain reliable predictions of fatigue lifetime and enable estimation of time-to-facture for any given stress level. In this article, relaxation of welding residual stresses as a function of cyclic loading was documented on three common steels: AISI 1008, ASTM A572, and AISI 4142. Welded specimens were subjected to cyclic bending (R = 0.1) at different applied stresses, and the residual stress relaxation existing near the welds was measured as a function of cycles. The steels exhibited very different stress relaxation behaviors during cyclic loadings, which can be related to the differences in the microstructures of the specimens. A phenomenological model, which treats dislocation motion during cyclic loading as being analogous to creep of dislocations, is proposed for estimation of the residual stress relaxation

Identification of a Thymic Epithelial Cell Subset Sharing Expression of the Class Ib HLA-G Molecule with Fetal Trophoblasts

HLA-G is the only class I determinant of the major histocompatibility complex (MHC) expressed by the trophoblasts, the fetal cells invading the maternal decidua during pregnancy. A unique feature of this nonclassical HLA molecule is its low polymorphism, a property that has been postulated to play an important role in preventing local activation of maternal alloreactive T and natural killer cells against the fetus. Yet, the mechanisms by which fetal HLA-G can be recognized as a self-MHC molecule by the maternal immune system remain unclear. Here we report the novel observation that HLA-G is expressed in the human thymus. Expression is targeted to the cell surface of thymic medullary and subcapsular epithelium. Thymic epithelial cell lines were generated and shown to express three alternatively spliced HLA-G transcripts, previously identified in human trophoblasts. Sequencing of HLA-G1 transcripts revealed a few nucleotide changes resulting in amino acid substitutions, all clustered within exon 3 of HLA-G, encoding for the α2 domain of the molecule. Our findings raise the possibility that maternal unresponsiveness to HLA-G–expressing fetal tissues may be shaped in the thymus by a previously unrecognized central presentation of this MHC molecule on the medullary epithelium

Diffusion MRI of Structural Brain Plasticity Induced by a Learning and Memory Task

Background: Activity-induced structural remodeling of dendritic spines and glial cells was recently proposed as an important factor in neuroplasticity and suggested to accompany the induction of long-term potentiation (LTP). Although T1 and diffusion MRI have been used to study structural changes resulting from long-term training, the cellular basis of the findings obtained and their relationship to neuroplasticity are poorly understood. Methodology/Principal Finding: Here we used diffusion tensor imaging (DTI) to examine the microstructural manifestations of neuroplasticity in rats that performed a spatial navigation task. We found that DTI can be used to define the selective localization of neuroplasticity induced by different tasks and that this process is age-dependent in cingulate cortex and corpus callosum and age-independent in the dentate gyrus. Conclusion/Significance: We relate the observed DTI changes to the structural plasticity that occurs in astrocytes and discuss the potential of MRI for probing structural neuroplasticity and hence indirectly localizing LTP

Bayesian Comparison of Neurovascular Coupling Models Using EEG-fMRI

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal

The neural correlates of dreaming.

Consciousness never fades during waking. However, when awakened from sleep, we sometimes recall dreams and sometimes recall no experiences. Traditionally, dreaming has been identified with rapid eye-movement (REM) sleep, characterized by wake-like, globally 'activated', high-frequency electroencephalographic activity. However, dreaming also occurs in non-REM (NREM) sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density electroencephalography, we contrasted the presence and absence of dreaming in NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with local decreases in low-frequency activity in posterior cortical regions. High-frequency activity in these regions correlated with specific dream contents. Monitoring this posterior 'hot zone' in real time predicted whether an individual reported dreaming or the absence of dream experiences during NREM sleep, suggesting that it may constitute a core correlate of conscious experiences in sleep