Gene therapy for allergic diseases

Airway diseases such as allergic asthma and rhinitis are characterized by a T-helper type 2 (Th2) response. Treatment of allergic airway diseases is currently limited to drugs that relieve disease symptoms and inflammation. In the search for new therapeutics, efforts have been made to treat allergic airway disease with gene therapy, and many preclinical studies have demonstrated its impressive potential. Most strategies focus on blocking the expression of proinflammatory proteins or transcription factors involved in the disease pathogenesis using antisense oligonucleotides, DNAzymes, small interfering RNA, or blocking of microRNAs using antagomirs. Changing the Th1/Th2 balance by overexpressing Th1-stimulating factors is another treatment option. Although the proof of concept is convincing in animal models, progress in humans remains limited. In this review, we focus on preclinical models to describe the recent developments and major breakthroughs for treating allergic airway diseases with gene therapy

Fayet-Iliopoulos terms in supergravity without gauged R-symmetry

We construct a supergravity-Maxwell theory with a novel embedding of the Fayet-Iliopoulos D-term, leading to spontaneous supersymmetry breaking. The gauging of the R-symmetry is not required and a gravitino mass is allowed for a generic vacuum. When matter couplings are introduced, an uplift through a positive definite contribution to the scalar potential is obtained. We observe a notable similarity to the $\overline{D3}$ uplift constructions and we give a natural description in terms of constrained multiplets.Comment: 23 pages, version 3, new appendix on electric-magnetic duality and updated reference

The role of the tachykinin NK1 receptor in airway changes in a mouse model of allergic asthma

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Supersymmetric embedding of antibrane polarization

We study the supersymmetry breaking induced by probe anti-D3-branes at the tip of the Klebanov-Strassler throat geometry. Antibranes inside this geometry polarize and can be described by an NS5-brane wrapping an $S^2$. When the number of antibranes is small compared to the background flux a metastable state exists that breaks supersymmetry. We present a manifestly supersymmetric effective model that realizes the polarized metastable state as a solution, spontaneously breaking the supersymmetry. The supersymmetric model relies crucially on the inclusion of Kaluza-Klein (matrix) degrees of freedom on the $S^2$ and two supersymmetric irrelevant deformations of ${\cal N}=4$ super-Yang-Mills (SYM), describing a large number of supersymmetric D3-branes in the IR. We explicitly identify the massless Goldstino and compute the spectrum of massive fluctuations around the metastable supersymmetry-breaking minimum, finding a Kaluza-Klein tower with masses warped down from the string scale. Below the Kaluza-Klein scale the massive tower can be integrated out and supersymmetry is realized nonlinearly. We comment on the effect of the Kaluza-Klein modes on the effective description of de Sitter vacua in string theory and inflationary model building.Comment: v1: 9 pages, v2: added references, changed title slightly and fixed typo

Noether Supercurrents, Supergravity and Broken Supersymmetry

Some general aspects of supersymmetry and supergravity are briefly reviewed with emphasis on Noether supercurrents and their role in the discussion of supersymmetry breaking.Comment: 12 pages. Contribution to the Proceedings of the Erice International School of Subnuclear Physics, 56th Course: "From gravitational waves to QED, QFD and QCD" Erice, 14-23 June 2018. v2 corrected typos and added reference

A Step Towards Uncovering The Structure of Multistable Neural Networks

We study the structure of multistable recurrent neural networks. The activation function is simplified by a nonsmooth Heaviside step function. This nonlinearity partitions the phase space into regions with different, yet linear dynamics. We derive how multistability is encoded within the network architecture. Stable states are identified by their semipositivity constraints on the synaptic weight matrix. The restrictions can be separated by their effects on the signs or the strengths of the connections. Exact results on network topology, sign stability, weight matrix factorization, pattern completion and pattern coupling are derived and proven. These may lay the foundation of more complex recurrent neural networks and neurocomputing.Comment: 33 pages, 9 figure