Statistical Fluctuations of Electromagnetic Transition Intensities and Electromagnetic Moments in pf-Shell Nuclei

We study the fluctuation properties of $\Delta T=0$ electromagnetic transition intensities and electromagnetic moments in $A \sim 60$ nuclei within the framework of the interacting shell model, using a realistic effective interaction for $pf$-shell nuclei with a $^{56}$Ni core. The distributions of the transition intensities and of the electromagnetic moments are well described by the Gaussian orthogonal ensemble of random matrices. In particular, the transition intensity distributions follow a Porter-Thomas distribution. When diagonal matrix elements (i.e., moments) are included in the analysis of transition intensities, we find that the distributions remain Porter-Thomas except for the isoscalar $M1$. The latter deviation is explained in terms of the structure of the isoscalar $M1$ operator.Comment: 11 pages, 4 figure

Endoscopic mucosal resection for colonic diffic ult sessile polyps and early superficial colonic tumo urs , an alternative effective method to colonic surgery . A Middle Eastern co untry experience

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Near-Optimal Quantum Algorithms for Multivariate Mean Estimation

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at extending the theory of multivariate sub-Gaussian estimators to the quantum setting. Unlike classically, where any univariate estimator can be turned into a multivariate estimator with at most a logarithmic overhead in the dimension, no similar result can be proved in the quantum setting. Indeed, Heinrich ruled out the existence of a quantum advantage for the mean estimation problem when the sample complexity is smaller than the dimension. Our main result is to show that, outside this low-precision regime, there is a quantum estimator that outperforms any classical estimator. Our approach is substantially more involved than in the univariate setting, where most quantum estimators rely only on phase estimation. We exploit a variety of additional algorithmic techniques such as amplitude amplification, the Bernstein-Vazirani algorithm, and quantum singular value transformation. Our analysis also uses concentration inequalities for multivariate truncated statistics. We develop our quantum estimators in two different input models that showed up in the literature before. The first one provides coherent access to the binary representation of the random variable and it encompasses the classical setting. In the second model, the random variable is directly encoded into the phases of quantum registers. This model arises naturally in many quantum algorithms but it is often incomparable to having classical samples. We adapt our techniques to these two settings and we show that the second model is strictly weaker for solving the mean estimation problem. Finally, we describe several applications of our algorithms, notably in measuring the expectation values of commuting observables and in the field of machine learning.Comment: 35 pages, 1 figure; v2: minor change

Effect of activated alloys on hydrogen discharge kinetics of MgH2 nanocrystals

This is the post-print version of the final paper published in Journal of Alloys and Compounds. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2007 Elsevier B.V.Activated alloys synthesized by arc-melting were examined as catalysts for improving the hydrogen sorption characteristics of nanostructured magnesium hydride, proposed as a reversible hydrogen storage material. The MgH2-catalyst absorbing materials were prepared by ball milling of pure MgH2 with hydrided Zr47Ni53, Zr9Ni11, and other investigated alloys. The nanostructured MgH2-intermetallic systems were tested at 250 °C and catalyst addition of eutectoid Zr47Ni53 resulted in the fastest desorption time and highest initial desorption rate. Also, the catalyzed Mg-hydride with activated Zr9Ni11 and Zr7Ni10 phases showed fast desorption kinetics. Moreover, the results demonstrated that the composition of dispersed ZrxNiy catalysts has a strong influence on the amount of accumulated hydrogen and desorption rate of Mg-nanocomposite.National Research Council Canad

Salivary microRNA 155, 146a/b and 203: A pilot study for potentially non-invasive diagnostic biomarkers of periodontitis and diabetes mellitus

Dysregulated expression of MicroRNAs (miRNAs) plays substantial role in the initiation and progression of both diabetes and periodontitis. The aim of the present study was to validate four miRNAs in saliva as potential predictive biomarkers of periodontal disease among patients with and without diabetes mellitus (DM). MiRNAs were extracted from the saliva of 24 adult subjects with DM and 29 healthy controls. Each group was subdivided into periodontally healthy or having periodontitis. In silico analysis identified 4 miRNAs (miRNA 155, 146 a/b and 203) as immune modulators. The expression of miRNAs-146a/b, 155, and 203 was tested using quantitative PCR. The expression levels in the study groups were compared to explore the effect of diabetes on periodontal status and vice versa. In our cohort, the four miRNAs expression were higher in patients with periodontitis and/or diabetes. miRNA-155 was the most reliable predictors of periodontitis among non-diabetics with an optimum cut-off value of < 8.97 with accuracy = 82.6%. MiRNA 146a, on the other hand, was the only reliable predictor of periodontitis among subjects with diabetes with optimum cut-off value of ≥11.04 with accuracy = 86.1%. The results of the present study concluded that MiRNA-146a and miRNA155 in saliva provide reliable, non-invasive, diagnostic and prognostic biomarkers that can be used to monitor periodontal health status among diabetic and non-diabetic patients

Statistical Fluctuations of Electromagnetic Transition Intensities in pf-Shell Nuclei

We study the fluctuation properties of E2 and M1 transition intensities among T=0,1 states of A = 60 nuclei in the framework of the interacting shell model, using a realistic effective interaction for pf-shell nuclei with a Ni56 as a core. It is found that the B(E2) distributions are well described by the Gaussian orthogonal ensemble of random matrices (Porter-Thomas distribution) independently of the isobaric quantum number T_z. However, the statistics of the B(M1) transitions is sensitive to T_z: T_z=1 nuclei exhibit a Porter-Thomas distribution, while a significant deviation from the GOE statistics is observed for self-conjugate nuclei (T_z=0).Comment: 8 pages, latex, 3 figures (ps format

Understanding the Role of Innate Immune Cells and Identifying Genes in Breast Cancer Microenvironment

The innate immune system is the first line of defense against invading pathogens and has a major role in clearing transformed cells, besides its essential role in activating the adaptive immune system. Macrophages, dendritic cells, NK cells, and granulocytes are part of the innate immune system that accumulate in the tumor microenvironment such as breast cancer. These cells induce inflammation in situ by secreting cytokines and chemokines that promote tumor growth and progression, in addition to orchestrating the activities of other immune cells. In breast cancer microenvironment, innate immune cells are skewed towards immunosuppression that may lead to tumor evasion. However, the mechanisms by which immune cells could interact with breast cancer cells are complex and not fully understood. Therefore, the importance of the mammary tumor microenvironment in the development, growth, and progression of cancer is widely recognized. With the advances of using bioinformatics and analyzing data from gene banks, several genes involved in NK cells of breast cancer individuals have been identified. In this review, we discuss the activities of certain genes involved in the cross-talk among NK cells and breast cancer. Consequently, altering tumor immune microenvironment can make breast tumors more responsive to immunotherapy

Bcl10 Regulates Lipopolysaccharide-Induced Pro-Fibrotic Signaling in Bronchial Fibroblasts from Severe Asthma Patients

Subepithelial fibrosis is a characteristic hallmark of airway remodeling in asthma. Current asthma medications have limited efficacy in treating fibrosis, particularly in patients with severe asthma, necessitating a deeper understanding of the fibrotic mechanisms. The NF-κB pathway is key to airway inflammation in asthma, as it regulates the activity of multiple pro-inflammatory mediators that contribute to airway pathology. Bcl10 is a well-known upstream mediator of the NF-κB pathway that has been linked to fibrosis in other disease models. Therefore, we investigated Bcl10-mediated NF-κB activation as a potential pathway regulating fibrotic signaling in severe asthmatic fibroblasts. We demonstrate here the elevated protein expression of Bcl10 in bronchial fibroblasts and bronchial biopsies from severe asthmatic patients when compared to non-asthmatic individuals. Lipopolysaccharide (LPS) induced the increased expression of the pro-fibrotic cytokines IL-6, IL-8 and TGF-β1 in bronchial fibroblasts, and this induction was associated with the activation of Bcl10. Inhibition of the Bcl10-mediated NF-κB pathway using an IRAK1/4 selective inhibitor abrogated the pro-fibrotic signaling induced by LPS. Thus, our study indicates that Bcl10-mediated NF-κB activation signals increased pro-fibrotic cytokine expression in severe asthmatic airways. This reveals the therapeutic potential of targeting Bcl10 signaling in ameliorating inflammation and fibrosis, particularly in severe asthmatic individuals