PROTECTION OF ISCHEMIA REPERFUSION INJURY IN MURINE LIVER BY GENE SILENCING OF COMPLEMENT 3

It has been documented that ischemia reperfusion injury (IRI) in the liver is associated with complement pathway activation. Thus, blocking complement 3 (C3) genes using a small hairpin RNA (shRNA) can potentially prevent liver IRI. Gene silencing of C3 in vivo was achieved via the administration of shRNA prior to IRI. Liver IRI was evaluated using histopathology, serum alanine aminotransferase (ALT), and aspartate aminotransferase (AST). Neutrophil accumulation was determined by myeloperoxidase assay. Oxidative stress was assessed by malondialdehyde and reactive oxygen species levels. In comparison with control mice treated with scrambled shRNA, the serum levels of ALT and AST were significantly reduced in mice treated with C3 shRNA. Additionally, neutrophil accumulation and lipid peroxidase-mediated tissue injury were decreased after shRNA treatment. Tissue histopathology showed an overall amelioration of injury in shRNA-treated mice. Therefore, the silencing of the C3 gene proved to be a potential therapy for preventing hepatic IRI

Diameters of symmetric and lifted simple exclusion models

We determine diameters of Markov chains describing one-dimensional N -particle models with an exclusion interaction, namely the Ssep (symmetric simple exclusion process) and one of its non-reversible liftings, the lifted Tasep (totally asymmetric simple exclusion process). The diameters provide lower bounds for the mixing times, and we discuss the implications of our findings for the analysis of these models

Rapid Mixing of Global Markov Chains via Spectral Independence: The Unbounded Degree Case

We consider spin systems on general n-vertex graphs of unbounded degree and explore the effects of spectral independence on the rate of convergence to equilibrium of global Markov chains. Spectral independence is a novel way of quantifying the decay of correlations in spin system models, which has significantly advanced the study of Markov chains for spin systems. We prove that whenever spectral independence holds, the popular Swendsen-Wang dynamics for the q-state ferromagnetic Potts model on graphs of maximum degree ?, where ? is allowed to grow with n, converges in O((? log n)^c) steps where c > 0 is a constant independent of ? and n. We also show a similar mixing time bound for the block dynamics of general spin systems, again assuming that spectral independence holds. Finally, for monotone spin systems such as the Ising model and the hardcore model on bipartite graphs, we show that spectral independence implies that the mixing time of the systematic scan dynamics is O(?^c log n) for a constant c > 0 independent of ? and n. Systematic scan dynamics are widely popular but are notoriously difficult to analyze. This result implies optimal O(log n) mixing time bounds for any systematic scan dynamics of the ferromagnetic Ising model on general graphs up to the tree uniqueness threshold. Our main technical contribution is an improved factorization of the entropy functional: this is the common starting point for all our proofs. Specifically, we establish the so-called k-partite factorization of entropy with a constant that depends polynomially on the maximum degree of the graph

The Critical Mean-Field Chayes-Machta Dynamics

The random-cluster model is a unifying framework for studying random graphs, spin systems and electrical networks that plays a fundamental role in designing efficient Markov Chain Monte Carlo (MCMC) sampling algorithms for the classical ferromagnetic Ising and Potts models. In this paper, we study a natural non-local Markov chain known as the Chayes-Machta dynamics for the mean-field case of the random-cluster model, where the underlying graph is the complete graph on n vertices. The random-cluster model is parametrized by an edge probability p and a cluster weight q. Our focus is on the critical regime: p = p_c(q) and q ? (1,2), where p_c(q) is the threshold corresponding to the order-disorder phase transition of the model. We show that the mixing time of the Chayes-Machta dynamics is O(log n ? log log n) in this parameter regime, which reveals that the dynamics does not undergo an exponential slowdown at criticality, a surprising fact that had been predicted (but not proved) by statistical physicists. This also provides a nearly optimal bound (up to the log log n factor) for the mixing time of the mean-field Chayes-Machta dynamics in the only regime of parameters where no non-trivial bound was previously known. Our proof consists of a multi-phased coupling argument that combines several key ingredients, including a new local limit theorem, a precise bound on the maximum of symmetric random walks with varying step sizes, and tailored estimates for critical random graphs. In addition, we derive an improved comparison inequality between the mixing time of the Chayes-Machta dynamics and that of the local Glauber dynamics on general graphs; this results in better mixing time bounds for the local dynamics in the mean-field setting

Characterization of Ba\u3csub\u3e1-x-y\u3c/sub\u3eCa\u3csub\u3ex\u3c/sub\u3eSr\u3csub\u3ey\u3c/sub\u3eTiO\u3csub\u3e3\u3c/sub\u3e Perovskites as Pb-Free Dielectric Materials

Use of lead-containing piezoelectric components in electrical and electronic devices has been banned on the EU market since July 1st, 2006. Development of lead-free high performance piezoelectric materials to meet the strong market demand is therefore imperative. In this paper, we report a systematic study on the structural, dielectric and ferroelectric properties of one class of lead-free piezoelectric materials, Ba1-x-yCaxSryTiO3 (x = 0-0.4, and y = 0-0.2) ceramics, using techniques such as XRD, SEM, impedance analyzer, and ferroelectric analyzer. It is found that with increasing Sr concentration in Ba1-ySryTiO3 and Ba0.8-ySryCa0.2TiO3, the crystal structure transforms from tetragonal to cubic along with a decreased unit-cell volume. The microstructures of all samples prepared are uniform and dense with the grain size decreasing with Sr content. The Curie temperature decreases faster with Sr and Ca co-doped BaTiO3 than that of Sr or Ca singularly-doped one. Above Curie temperature, a tunability of 31.4% can be achieved at an applied voltage of 30 kV/cm for (Ba0.6Ca0.2Sr0.2TiO3). These properties promise Ba1-x-yCaxSryTiO3 system to be applicable in Pb-free tunable devices

Dermatomyositis with intrahepatic cholangiocarcinoma: a case report and data mining based on machine learning

Cancer secondary to dermatomyositis (DM) is defined as paraneoplastic dermatomyositis, which is one of the major subtypes of DM. However, cases of DM with intrahepatic cholangiocarcinoma (ICC) are rarely reported. In the course of our clinical work, we encountered a case of a middle-aged female patient who was diagnosed with DM for 7 years and then diagnosed with ICC, and we would like to share this case. In addition, in order to further investigate the deeper mechanism of ICC associated with DM, we also analyzed the dataset related to DM and ICC in the Gene Expression Omnibus (GEO) database based on the machine learning methods and found that poly(ADP-ribose) polymerase family member 12 (PARP12) and metallothionein 1M (MT1M) were closely associated with ICC secondary to DM. They are potentially important biomarkers for predicting the occurrence of ICC in patients with DM

Variational quantum circuit learning of entanglement purification in multi-degree-of-freedom

Quantum entanglement purification (EP) is a crucial technique for promising the effective function of entanglement channel in noisy large-scale quantum network. The previous EP protocols lack of a general circuit framework and become complicated to design in high-dimensional cases. In this paper, we propose a variational quantum circuit framework and demonstrate its feasibility of learning optimal protocols of EP in multi-degree-of-freedom (DoF). By innovatively introducing the additional circuit lines for representing the ancillary DoFs, e.g. space and time, the parameterized quantum circuit can effectively simulate the scalable EP process. As examples, well-known protocols in linear optics including PSBZ, HHSZ+ and etc., are learnt successfully with high fidelities and the alternative equivalent operations are discovered in low-depth quantum circuit. Our work pays the way for exploring the EP protocols with multi-DoF by quantum machine learning.Comment: 8 pages, 5 figures, comments are welcome

One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense