Estrogen Protects the Female Heart from Ischemia/Reperfusion Injury through Manganese Superoxide Dismutase Phosphorylation by Mitochondrial p38β at Threonine 79 and Serine 106.

A collective body of evidence indicates that estrogen protects the heart from myocardial ischemia/reperfusion (I/R) injury, but the underlying mechanism remains incompletely understood. We have previously delineated a novel mechanism of how 17β-estradiol (E2) protects cultured neonatal rat cardiomyocytes from hypoxia/reoxygenation (H/R) by identifying a functionally active mitochondrial pool of p38β and E2-driven upregulation of manganese superoxide dismutase (MnSOD) activity via p38β, leading to the suppression of reactive oxygen species (ROS) and apoptosis. Here we investigate these cytoprotective actions of E2 in vivo. Left coronary artery ligation and reperfusion was used to produce I/R injury in ovariectomized (OVX) female mice and in estrogen receptor (ER) null female mice. E2 treatment in OVX mice reduced the left ventricular infarct size accompanied by increased activity of mitochondrial p38β and MnSOD. I/R-induced infarct size in ERα knockout (ERKO), ERβ knockout (BERKO) and ERα and β double knockout (DERKO) female mice was larger than that in wild type (WT) mice, with little difference among ERKO, BERKO, and DERKO. Loss of both ERα and ERβ led to reduced activity of mitochondrial p38β and MnSOD at baseline and after I/R. The physical interaction between mitochondrial p38β and MnSOD in the heart was detected by co-immunoprecipitation (co-IP). Threonine 79 (T79) and serine 106 (S106) of MnSOD were identified to be phosphorylated by p38β in kinase assays. Overexpression of WT MnSOD in cardiomyocytes reduced ROS generation during H/R, while point mutation of T79 and S106 of MnSOD to alanine abolished its antioxidative function. We conclude that the protective effects of E2 and ER against cardiac I/R injury involve the regulation of MnSOD via posttranslational modification of the dismutase by p38β

Pseudo-goldstino and electroweakinos via VBF processes at LHC

The multi-sector SUSY breaking predicts pseudo-goldstino which can couple to the visible sector more strongly than the ordinary gavitino and thus induce the decays of the lightest neutralino and chargino (collectively called electroweakinos) inside the detector. In this note we study the electroweakino pair productions via vector boson fusion (VBF) processes followed by decays to pseudo-goldstino at the LHC. Our Monte Carlo simulations show that at the 14 TeV LHC with 3000 fb^{-1} luminosity the dominant production channel pp->chargino+neutralino+2 jets can have a statistical significance above 2-sigma while other production channels are not accessible.Comment: Version in JHEP (comments added

Differentiable Programming Tensor Networks

Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD). The concept emerges from deep learning but is not only limited to training neural networks. We present theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher order derivatives of the program accurately and efficiently using AD. We present essential techniques to differentiate through the tensor networks contractions, including stable AD for tensor decomposition and efficient backpropagation through fixed point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient based variational optimization of infinite projected entangled pair states for quantum antiferromagnetic Heisenberg model and obtain start-of-the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.Comment: Typos corrected, discussion and refs added; revised version accepted for publication in PRX. Source code available at https://github.com/wangleiphy/tensorgra

Pilot Power Allocation Through User Grouping in Multi-Cell Massive MIMO Systems

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation $\rm {Exp}_{rcee}$ and the achievable uplink rate holding for any number of base station antennas $M$, with the least squares (LS) and minimum mean squared error (MMSE) estimation methods. It is found that RCEE and $\rm {Exp}_{rcee}$ converge to the same constant value when $M\rightarrow\infty$, resulting in the pilot power allocation (PPA) is substantially simplified and a PPA algorithm is proposed to minimize the average $\rm {Exp}_{rcee}$ per user with a total pilot power budget $P$ in multi-cell massive multiple-input multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are only significant with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large $P$, the performance of the LS approaches to the performance of the MMSE, which means that simple LS estimation method is a very viable when co-channel interference is small. For the achievable uplink rate, the PPA scheme delivers almost the same average achievable uplink rate and improves the minimum achievable uplink rate compared with the EPPA scheme.Comment: 30 pages, 5 figures, submitted to IEEE Transactions on Communication