Performance Limits and Geometric Properties of Array Localization

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cram\'er-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.Comment: to appear in IEEE Transactions on Information Theor

Mice with enhanced macrophage angiotensin-converting enzyme are resistant to melanoma

Journal ArticleAngiotensin-converting enzyne (ACE) is a pepitdase responsible for the cleavage of angiotensin I and Several other peptides. Here, gene targeting was used to switch control of the ACE locus from the endogenous promoter to the macrophage-specific c-fms promoter. Challenge of these mice, called ACE 10/10, with the aggressive mouse melanoma cell line B16 showed that they are remarkably resistant to tumor growth

A study on the inhibitory effect of Solanum lyratum thumb extract on lewis lung carcinoma lines

The objective of this paper was to observe the effects of Solanum lyratum Thunb extract on tumour inhibition, immune function and survival time of tumour-bearing mice. Lung carcinoma-bearing mouse model was  established, the tumour-bearing mice were divided into model group, CTX group, Solanum lyratum Thunb extract high-dose group and low-dose group. By the examination of tumour inhibition rate of Solanum lyratum Thunb extract in Lewis lung carcinoma-bearing mice and determination of the number of NK cells and T cell subsets, the survival rate of tumour-bearing mice was observed. Solanum lyratum Thunb extract had some anti-tumour effect in Lewis tumour-bearing mice. The tumour inhibition rate of high-dose group reached 46.28%, and the tumour inhibition rate of low-dose group was 31.42%. Solanum lyratum Thunb extract can improve the NK cell activity of Lewis tumour-bearing mice, increase the number of CD4 cells in the tumour-bearing mice, and significantly increase the  survival rate of tumour-bearing mice. The study concluded that Solanum lyratum Thunb extract has some anti-tumour effect and can improve  immune function and survival rate of tumour-bearing mice.Keywords: Solanum lyratum Thunb; tumour-bearing mice; anti-tumour effec

JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI

Multi-contrast Magnetic Resonance Imaging (MRI) generates multiple medical images with rich and complementary information for routine clinical use; however, it suffers from a long acquisition time. Recent works for accelerating MRI, mainly designed for single contrast, may not be optimal for multi-contrast scenario since the inherent correlations among the multi-contrast images are not exploited. In addition, independent reconstruction of each contrast usually does not translate to optimal performance of downstream tasks. Motivated by these aspects, in this paper we design an end-to-end framework for accelerating multi-contrast MRI which simultaneously optimizes the entire MR imaging workflow including sampling, reconstruction and downstream tasks to achieve the best overall outcomes. The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way. The sampling mask generator and the reconstructor are trained jointly across the multiple image contrasts. The acceleration ratio of each image contrast is also learnable and can be driven by a downstream task performance. We validate our approach on a multi-contrast brain dataset and a multi-contrast knee dataset. Experiments show that (1) our framework consistently outperforms the baselines designed for single contrast on both datasets; (2) our newly designed recurrent reconstruction network effectively improves the reconstruction quality for multi-contrast images; (3) the learnable acceleration ratio improves the downstream task performance significantly. Overall, this work has potentials to open up new avenues for optimizing the entire multi-contrast MR imaging workflow

Theory of Underdoped Cuprates

We develop a slave-boson theory for the t-J model at finite doping which respects an SU(2) symmetry -- a symmetry previously known to be important at half filling. The mean field phase diagram is found to be consistent with the phases observed in the cuprate superconductors, which contains d-wave superconductor, spin gap, strange metal, and Fermi liquid phases. The spin gap phase is best understood as the staggered flux phase, which is nevertheless translationally invariant for physical quantities. The electron spectral function shows small Fermi pockets at low doping which continuously evolve into the large Fermi surface at high doping concentrations.Comment: 4 pages, latex(revtex,epsf), 3 figure