Adipose Tissue Remodeling during Cancer Cachexia

Cancer-induced cachexia (CC), characterized by systemic inflammation, body weight loss, adipose tissue (AT) remodeling, and muscle wasting, is a malignant metabolic syndrome with an undefined etiology. There is a consensus that multiple factors contribute to cancer-induced AT remodeling, and longitudinal studies show that patients lose AT before they start losing muscle mass. In CC, AT remodeling occurs predominantly through adipocyte atrophy, impairment of fatty acid turnover, inflammation, rearrangement of extracellular matrix (ECM), and browning of AT. More recently, some studies have shown that AT is affected early in the course of cachexia. Additionally, studies using experimental models have consistently indicated that the alterations in adipocyte metabolism begin quite early, followed by the downregulation of adipogenic and thermogenic genes. These sets of changes, in addition to metabolites derived from this process, maybe the initial (sterile) trigger of the sequence of events that result in the remodeling and dysfunction of AT in cachexia. Therefore, the present chapter aims to describe state of the art related to the subject of interest by analyzing the primary studies that have addressed the possible interface between inflammation and morphofunctional alterations of AT, in addition to the possible repercussions of this process during the development of CC

Adipose Tissue Inflammation and Metabolic Disorders

Adipose tissue not only possesses an important role in the storage of excess nutrients but also acts as a critical immune and endocrine organ. Researchers and clinicians now consider adipose tissue to be an active endocrine organ that secretes various humoral factors called “adipokines,” which imparts important systemic metabolic effects, from food intake to glucose tolerance. Along with its production of specialized adipokines, adipose tissue also secretes proinflammatory cytokines that likely contributes to the low-level systemic inflammation that has become a hallmark of various metabolic syndrome-associated chronic pathologies, such as obesity and cancer cachexia. These systemic effects may be mediated by communication networks arising from the multitude of resident adipose cells, including adipocytes, endothelial cells, neuronal cells, stem cells and other precursors, and a wide variety of immune cell populations that recent studies have demonstrated play a crucial role in the development of adipose inflammation and systemic metabolic abnormalities. In this chapter, we detail various molecular pathways linking excess adipose lipid storage to chronic inflammation and review the current knowledge as to what triggers obesity- and cachexia-associated inflammation in adipose tissue. Finally, we describe how the cross talk between adipose tissue inflammation and the non-adipocyte resident cells present in tissue is involved in this metabolic disruption

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions

Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV

info:eu-repo/semantics/publishe

Evidence for the Higgs boson decay to a bottom quark–antiquark pair

info:eu-repo/semantics/publishe

Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions

info:eu-repo/semantics/publishe

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis