Angiogenic deficiency and adipose tissue dysfunction are associated with macrophage malfunction in SIRT1 \u3csup\u3e-/-\u3c/sup\u3e mice

The histone deacetylase sirtuin 1 (SIRT1) inhibits adipocyte differentiation and suppresses inflammation by targeting the transcription factors peroxisome proliferator-activated receptor γ and nuclear factor κB. Although this suggests that adiposity and inflammation should be enhanced when SIRT1 activity is inactivated in the body, this hypothesis has not been tested in SIRT1 null (SIRT1 -/-) mice. In this study, we addressed this issue by investigating the adipose tissue in SIRT1 -/-mice. Compared with their wild-type littermates, SIRT1 null mice exhibited a significant reduction in body weight. In adipose tissue, the average size of adipocytes was smaller, the content of extracellular matrix was lower, adiponectin and leptin were expressed at 60% of normal level, and adipocyte differentiation was reduced. All of these changes were observed with a 50% reduction in capillary density that was determined using a three-dimensional imaging technique. Except for vascular endothelial growth factor, the expression of several angiogenic factors (Pdgf, Hgf, endothelin, apelin, and Tgf-β)was reduced by about 50%. Macrophage infiltration and inflammatory cytokine expression were 70% less in the adipose tissue of null mice and macrophage differentiation was significantly inhibited in SIRT1 -/- mouse embryonic fibroblasts in vitro. In wild-type mice, macrophage deletion led to a reduction in vascular density. These data suggest that SIRT1 controls adipose tissue function through regulation of angiogenesis, whose deficiency is associated with macrophage malfunction in SIRT1 -/- mice. The study supports the concept that inflammation regulates angiogenesis in the adipose tissue. Copyright © 2012 by The Endocrine Society

Deep Dictionary Learning with An Intra-class Constraint

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods.Comment: 6 pages, 3 figures, 2 tables. It has been accepted in ICME202

Correlational Analysis of Sarcopenia and Multimorbidity Among Older Inpatients

BACKGROUND: Sarcopenia and multimorbidity are common in older adults, and most of the available clinical studies have focused on the relationship between specialist disorders and sarcopenia, whereas fewer studies have been conducted on the relationship between sarcopenia and multimorbidity. We therefore wished to explore the relationship between the two. METHODS: The study subjects were older patients (aged ≥ 65 years) who were hospitalized at the Department of Geriatrics of the First Affiliated Hospital of Chongqing Medical University between March 2016 and September 2021. Their medical records were collected. Based on the diagnostic criteria of the Asian Sarcopenia Working Group in 2019, the relationship between sarcopenia and multimorbidity was elucidated. RESULTS: 1.A total of 651 older patients aged 65 years and above with 2 or more chronic diseases were investigated in this study, 46.4% were suffering from sarcopenia. 2. Analysis of the relationship between the number of chronic diseases and sarcopenia yielded that the risk of sarcopenia with 4-5 chronic diseases was 1.80 times higher than the risk of 2-3 chronic diseases (OR 1.80, 95%CI 0.29-2.50), and the risk of sarcopenia with ≥ 6 chronic diseases was 5.11 times higher than the risk of 2-3 chronic diseases (OR 5.11, 95% CI 2.97-9.08), which remained statistically significant, after adjusting for relevant factors. 3. The Charlson comorbidity index was associated with skeletal muscle mass index, handgrip strength, and 6-meter walking speed, with scores reaching 5 and above suggesting the possibility of sarcopenia. 4. After adjusting for some covariates among 14 common chronic diseases in older adults, diabetes (OR 3.20, 95% CI 2.01-5.09), cerebrovascular diseases (OR 2.07, 95% CI 1.33-3.22), bone and joint diseases (OR 2.04, 95% CI 1.32-3.14), and malignant tumors (OR 2.65, 95% CI 1.17-6.55) were among those that still a risk factor for the development of sarcopenia. CONCLUSION: In the hospitalized older adults, the more chronic diseases they have, the higher the prevalence of sarcopenia. When the CCI is 5, attention needs to be paid to the occurrence of sarcopenia in hospitalized older adults

An Operational Matrix Technique for Solving Variable Order Fractional Differential-Integral Equation Based on the Second Kind of Chebyshev Polynomials

An operational matrix technique is proposed to solve variable order fractional differential-integral equation based on the second kind of Chebyshev polynomials in this paper. The differential operational matrix and integral operational matrix are derived based on the second kind of Chebyshev polynomials. Using two types of operational matrixes, the original equation is transformed into the arithmetic product of several dependent matrixes, which can be viewed as an algebraic system after adopting the collocation points. Further, numerical solution of original equation is obtained by solving the algebraic system. Finally, several examples show that the numerical algorithm is computationally efficient

An Operational Matrix of Fractional Differentiation of the Second Kind of Chebyshev Polynomial for Solving Multiterm Variable Order Fractional Differential Equation

The multiterm fractional differential equation has a wide application in engineering problems. Therefore, we propose a method to solve multiterm variable order fractional differential equation based on the second kind of Chebyshev Polynomial. The main idea of this method is that we derive a kind of operational matrix of variable order fractional derivative for the second kind of Chebyshev Polynomial. With the operational matrices, the equation is transformed into the products of several dependent matrices, which can also be viewed as an algebraic system by making use of the collocation points. By solving the algebraic system, the numerical solution of original equation is acquired. Numerical examples show that only a small number of the second kinds of Chebyshev Polynomials are needed to obtain a satisfactory result, which demonstrates the validity of this method

Existence results of positive solutions for Kirchhoff type equations via bifurcation methods

In this paper we address the following Kirchhoff type problem \begin{equation*} \left\{ \begin{array}{ll} -\Delta(g(|\nabla u|_2^2) u + u^r) = a u + b u^p& \mbox{in}~\Omega, u>0& \mbox{in}~\Omega, u= 0& \mbox{on}~\partial\Omega, \end{array} \right. \end{equation*} in a bounded and smooth domain $\Omega$ in ${\rm I}\hskip -0.85mm{\rm R}$. By using change of variables and bifurcation methods, we show, under suitable conditions on the parameters $a,b,p,r$ and the nonlinearity $g$, the existence of positive solutions.Comment: 18 pages, 1 figur

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).Comment: accepted by CVPR202