A theory of ferromagnetism in planar heterostructures of (Mn,III)-V semiconductors

A density functional theory of ferromagnetism in heterostructures of compound semiconductors doped with magnetic impurities is presented. The variable functions in the density functional theory are the charge and spin densities of the itinerant carriers and the charge and localized spins of the impurities. The theory is applied to study the Curie temperature of planar heterostructures of III-V semiconductors doped with manganese atoms. The mean-field, virtual-crystal and effective-mass approximations are adopted to calculate the electronic structure, including the spin-orbit interaction, and the magnetic susceptibilities, leading to the Curie temperature. By means of these results, we attempt to understand the observed dependence of the Curie temperature of planar $\delta$-doped ferromagnetic structures on variation of their properties. We predict a large increase of the Curie Temperature by additional confinement of the holes in a $\delta$-doped layer of Mn by a quantum well.Comment: 8 pages, 7 figure

Ferromagnetism in semiconductors and oxides: prospects from a ten years' perspective

Over the last decade the search for compounds combining the resources of semiconductors and ferromagnets has evolved into an important field of materials science. This endeavour has been fuelled by continual demonstrations of remarkable low-temperature functionalities found for ferromagnetic structures of (Ga,Mn)As, p-(Cd,Mn)Te, and related compounds as well as by ample observations of ferromagnetic signatures at high temperatures in a number of non-metallic systems. In this paper, recent experimental and theoretical developments are reviewed emphasising that, from the one hand, they disentangle many controversies and puzzles accumulated over the last decade and, on the other, offer new research prospects.Comment: review, 13 pages, 8 figures, 109 reference

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

An experimental study of the intrinsic stability of random forest variable importance measures

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets

Nonlinear internal waves over New Jersey's continental shelf

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): C03022, doi:10.1029/2010JC006332.Ship and mooring data collected off the coast of New Jersey are used to describe the nonlinear internal wave (NLIW) field and the background oceanographic conditions that formed the waveguide on the shelf. The subinertial, inertial, and tidal circulation are described in detail, and the background fluid state is characterized using the coefficients of the extended Korteweg–de Vries equation. The utility of this type of analysis is demonstrated in description of an amplitude-limited, flat wave. NLIWs observed over most of the month had typical displacements of −8 m, but waves observed from 17–21 August were almost twice as large with displacements near −15 m. During most of the month, wave packets occurred irregularly at a fixed location, and often more than one packet was observed per M2 tidal period. In contrast, the arrival times of the large-amplitude wave groups observed over 17–21 August were more closely phased with the barotropic tide. The time span in which the largest NLIWs were observed corresponded to neap barotropic conditions, but when the shoreward baroclinic energy flux was elevated. During the time of large NLIWs, near-inertial waves were a dominate contributor to the internal motions on the shelf and apparently regulated wave formation, as destructive/constructive modulation of the M2 internal tide by the inertial wavefield at the shelf break corresponded to stronger/weaker NLIWs on the shelf.This work was funded by the Office of Naval Research

Network deconvolution as a general method to distinguish direct dependencies in networks

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.National Institutes of Health (U.S.) (grant R01 HG004037)National Institutes of Health (U.S.) (grant HG005639)Swiss National Science Foundation (Fellowship)National Science Foundation (U.S.) (NSF CAREER Award 0644282

Visual motion with pink noise induces predation behaviour

Visual motion cues are one of the most important factors for eliciting animal behaviour, including predator-prey interactions in aquatic environments. To understand the elements of motion that cause such selective predation behaviour, we used a virtual plankton system where the predation behaviour in response to computer-generated prey was analysed. First, we performed motion analysis of zooplankton (Daphnia magna) to extract mathematical functions for biologically relevant motions of prey. Next, virtual prey models were programmed on a computer and presented to medaka (Oryzias latipes), which served as predatory fish. Medaka exhibited predation behaviour against several characteristic virtual plankton movements, particularly against a swimming pattern that could be characterised as pink noise motion. Analysing prey-predator interactions via pink noise motion will be an interesting research field in the future

Irf4 is a positional and functional candidate gene for the control of serum IgM levels in the mouse

Natural IgM are involved in numerous immunological functions but the genetic factors that control the homeostasis of its secretion and upholding remain unknown. Prompted by the finding that C57BL/6 mice had significantly lower serum levels of IgM when compared with BALB/c mice, we performed a genome-wide screen and found that the level of serum IgM was controlled by a QTL on chromosome 13 reaching the highest level of association at marker D13Mit266 (LOD score¼3.54). This locus was named IgMSC1 and covered a region encompassing the interferon-regulatory factor 4 gene (Irf4). The number of splenic mature B cells in C57BL/6 did not differ from BALB/c mice but we found that low serum levels of IgM in C57BL/6 mice correlated with lower frequency of IgM-secreting cells in the spleen and in the peritoneal cavity. These results suggested that C57BL/6 mice have lower efficiency in late B-cell maturation, a process that is highly impaired in Irf4 knockout mice. In fact, we also found reduced Irf4 gene expression in B cells of C57BL/6 mice. Thus, we propose Irf4 as a candidate for the IgMSC1 locus, which controls IgM homeostatic levels at the level of B-cell terminal differentiation

Self-Reactivities to the Non-Erythroid Alpha Spectrin Correlate with Cerebral Malaria in Gabonese Children

BACKGROUND: Hypergammaglobulinemia and polyclonal B-cell activation commonly occur in Plasmodium sp. infections. Some of the antibodies produced recognize self-components and are correlated with disease severity in P. falciparum malaria. However, it is not known whether some self-reactive antibodies produced during P. falciparum infection contribute to the events leading to cerebral malaria (CM). We show here a correlation between self-antibody responses to a human brain protein and high levels of circulating TNF alpha (TNFα), with the manifestation of CM in Gabonese children. METHODOLOGY: To study the role of self-reactive antibodies associated to the development of P. falciparum cerebral malaria, we used a combination of quantitative immunoblotting and multivariate analysis to analyse correlation between the reactivity of circulating IgG with a human brain protein extract and TNFα concentrations in cohorts of uninfected controls (UI) and P. falciparum-infected Gabonese children developing uncomplicated malaria (UM), severe non-cerebral malaria (SNCM), or CM. RESULTS/CONCLUSION: The repertoire of brain antigens recognized by plasma IgGs was more diverse in infected than in UI individuals. Anti-brain reactivity was significantly higher in the CM group than in the UM and SNCM groups. IgG self-reactivity to brain antigens was also correlated with plasma IgG levels and age. We found that 90% of CM patients displayed reactivity to a high-molecular mass band containing the spectrin non-erythroid alpha chain. Reactivity with this band was correlated with high TNFα concentrations in CM patients. These results strongly suggest that an antibody response to brain antigens induced by P. falciparum infection may be associated with pathogenic mechanisms in patients developing CM