Magnetoelectric Coupling and Electric Control of Magnetization in Ferromagnet-Ferroelectric-Metal Superlattices

Ferromagnet-ferroelectric-metal superlattices are proposed to realize the large room-temperature magnetoelectric effect. Spin dependent electron screening is the fundamental mechanism at the microscopic level. We also predict an electric control of magnetization in this structure. The naturally broken inversion symmetry in our tri-component structure introduces a magnetoelectric coupling energy of $P M^2$. Such a magnetoelectric coupling effect is general in ferromagnet-ferroelectric heterostructures, independent of particular chemical or physical bonding, and will play an important role in the field of multiferroics.Comment: 5 pages including 3 figures and 1 tabl

Generation of Relativistic Electron Bunches with Arbitrary Current Distribution via Transverse-to-Longitudinal Phase Space Exchange

We propose a general method for tailoring the current distribution of relativistic electron bunches. The technique relies on a recently proposed method to exchange the longitudinal phase space emittance with one of the transverse emittances. The method consists of transversely shaping the bunch and then converting its transverse profile into a current profile via a transverse-to-longitudinal phase-space-exchange beamline. We show that it is possible to tailor the current profile to follow, in principle, any desired distributions. We demonstrate, via computer simulations, the application of the method to generate trains of microbunches with tunable spacing and linearly-ramped current profiles. We also briefly explore potential applications of the technique.Comment: 13 pages, 17 figure

Singular values of some modular functions

We study the properties of special values of the modular functions obtained from Weierstrass P-function at imaginary quadratic points.Comment: 19 pages,corrected typo

An explicit height bound for the classical modular polynomial

For a prime m, let Phi_m be the classical modular polynomial, and let h(Phi_m) denote its logarithmic height. By specializing a theorem of Cohen, we prove that h(Phi_m) <= 6 m log m + 16 m + 14 sqrt m log m. As a corollary, we find that h(Phi_m) <= 6 m log m + 18 m also holds. A table of h(Phi_m) values is provided for m <= 3607.Comment: Minor correction to the constants in Theorem 1 and Corollary 9. To appear in the Ramanujan Journal. 17 pages

Interacting Constituents in Cosmology

Universe evolution, as described by Friedmann's equations, is determined by source terms fixed by the choice of pressure $\times$ energy-density equations of state $p(\rho)$. The usual approach in Cosmology considers equations of state accounting only for kinematic terms, ignoring the contribution from the interactions between the particles constituting the source fluid. In this work the importance of these neglected terms is emphasized. A systematic method, based on the Statistical Mechanics of real fluids, is proposed to include them. A toy-model is presented which shows how such interaction terms can engender significant cosmological effects.Comment: 24 pages, 6 figures. It includes results presented in "Cosmic Acceleration from Elementary Interactions" [arXiv:gr-qc/0512135]. Citations added in v.

A hop-count based positioning algorithm for wireless ad-hoc networks

We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hop-count metric’s ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm’s performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing received signal strength and expected transmission count. We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm’s performance through simulation

Differential modulatory effects of GSK-3β and HDM2 on sorafenib-induced AIF nuclear translocation (programmed necrosis) in melanoma

<p>Abstract</p> <p>Background</p> <p>GSK-3β phosphorylates numerous substrates that govern cell survival. It phosphorylates p53, for example, and induces its nuclear export, HDM2-dependent ubiquitination, and proteasomal degradation. GSK-3β can either enhance or inhibit programmed cell death, depending on the nature of the pro-apoptotic stimulus. We previously showed that the multikinase inhibitor sorafenib activated GSK-3β and that this activation attenuated the cytotoxic effects of the drug in various BRAF-mutant melanoma cell lines. In this report, we describe the results of studies exploring the effects of GSK-3β on the cytotoxicity and antitumor activity of sorafenib combined with the HDM2 antagonist MI-319.</p> <p>Results</p> <p>MI-319 alone increased p53 levels and p53-dependent gene expression in melanoma cells but did not induce programmed cell death. Its cytotoxicity, however, was augmented in some melanoma cell lines by the addition of sorafenib. In responsive cell lines, the MI-319/sorafenib combination induced the disappearance of p53 from the nucleus, the down modulation of Bcl-2 and Bcl-x_L, the translocation of p53 to the mitochondria and that of AIF to the nuclei. These events were all GSK-3β-dependent in that they were blocked with a GSK-3β shRNA and facilitated in otherwise unresponsive melanoma cell lines by the introduction of a constitutively active form of the kinase (GSK-3β-S9A). These modulatory effects of GSK-3β on the activities of the sorafenib/MI-319 combination were the exact reverse of its effects on the activities of sorafenib alone, which induced the down modulation of Bcl-2 and Bcl-x_Land the nuclear translocation of AIF only in cells in which GSK-3β activity was either down modulated or constitutively low. In A375 xenografts, the antitumor effects of sorafenib and MI-319 were additive and associated with the down modulation of Bcl-2 and Bcl-x_L, the nuclear translocation of AIF, and increased suppression of tumor angiogenesis.</p> <p>Conclusions</p> <p>Our data demonstrate a complex partnership between GSK-3β and HDM2 in the regulation of p53 function in the nucleus and mitochondria. The data suggest that the ability of sorafenib to activate GSK-3β and alter the intracellular distribution of p53 may be exploitable as an adjunct to agents that prevent the HDM2-dependent degradation of p53 in the treatment of melanoma.</p

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance