Mechanisms of dysregulation of low-density lipoprotein receptor expression in vascular smooth muscle cells by inflammatory cytokines

Objective - Although inflammation is a recognized feature of atherosclerosis, the impact of inflammation on cellular cholesterol homeostasis is unclear. This study focuses on the molecular mechanisms by which inflammatory cytokines disrupt low-density lipoprotein (LDL) receptor regulation.Methods and Results - IL-1 beta enhanced transformation of vascular smooth muscle cells into foam cells by increasing uptake of unmodified LDL via LDL receptors and by enhancing cholesterol esterification as demonstrated by Oil Red O staining and direct assay of intracellular cholesterol concentrations. In the absence of IL-1 beta, a high concentration of LDL decreased LDL receptor promoter activity, mRNA synthesis and protein expression. However, IL-1 beta enhanced LDL receptor expression, overriding the suppression usually induced by a high concentration of LDL and inappropriately increasing LDL uptake. Exposure to IL-1 beta also caused overexpression of the sterol regulatory element binding protein ( SREBP) cleavage-activating protein ( SCAP), and enhanced its translocation from the endoplasmic reticulum to the Golgi, where it is known to cleave SREBP, thereby enhancing LDL receptor gene expression.Conclusions - These observations demonstrate that IL-1 beta disrupts cholesterol-mediated LDL receptor feedback regulation, permitting intracellular accumulation of unmodified LDL and causing foam cell formation. The implication of these findings is that inflammatory cytokines may contribute to intracellular LDL accumulation without previous modification of the lipoprotein

A review of physical supply and EROI of fossil fuels in China

This paper reviews China’s future fossil fuel supply from the perspectives of physical output and net energy output. Comprehensive analyses of physical output of fossil fuels suggest that China’s total oil production will likely reach its peak, at about 230 Mt/year (or 9.6 EJ/year), in 2018; its total gas production will peak at around 350 Bcm/year (or 13.6 EJ/year) in 2040, while coal production will peak at about 4400 Mt/year (or 91.9 EJ/year) around 2020 or so. In terms of the forecast production of these fuels, there are significant differences among current studies. These differences can be mainly explained by different ultimately recoverable resources assumptions, the nature of the models used, and differences in the historical production data. Due to the future constraints on fossil fuels production, a large gap is projected to grow between domestic supply and demand, which will need to be met by increasing imports. Net energy analyses show that both coal and oil and gas production show a steady declining trend of EROI (energy return on investment) due to the depletion of shallow-buried coal resources and conventional oil and gas resources, which is generally consistent with the approaching peaks of physical production of fossil fuels. The peaks of fossil fuels production, coupled with the decline in EROI ratios, are likely to challenge the sustainable development of Chinese society unless new abundant energy resources with high EROI values can be found

Electrochemically controlled rectification in symmetric single-molecule junctions.

Single-molecule electrochemical science has advanced over the past decades and now extends well beyond molecular imaging, to molecular electronics functions such as rectification and amplification. Rectification is conceptually the simplest but has involved mostly challenging chemical synthesis of asymmetric molecular structures or asymmetric materials and geometry of the two enclosing electrodes. Here we propose an experimental and theoretical strategy for building and tuning in situ (in operando) rectification in two symmetric molecular structures in electrochemical environment. The molecules were designed to conduct electronically via either their lowest unoccupied molecular orbital (LUMO; electron transfer) or highest occupied molecular orbital (HOMO; "hole transfer"). We used a bipotentiostat to control separately the electrochemical potential of the tip and substrate electrodes of an electrochemical scanning tunneling microscope (EC-STM), which leads to independent energy alignment of the STM tip, the molecule, and the STM substrate. By creating an asymmetric energy alignment, we observed single-molecule rectification of each molecule within a voltage range of ±0.5 V. By varying both the dominating charge transporting LUMO or HOMO energy and the electrolyte concentration, we achieved tuning of the polarity as well as the amplitude of the rectification. We have extended an earlier proposed theory that predicts electrolyte-controlled rectification to rationalize all the observed in situ rectification features and found excellent agreement between theory and experiments. Our study thus offers a way toward building controllable single-molecule rectifying devices without involving asymmetric molecular structures

Light dark matter and Z′Z' dark force at colliders

Light Dark Matter, $<10$ GeV, with sizable direct detection rate is an interesting and less explored scenario. Collider searches can be very powerful, such as through the channel in which a pair of dark matter particle are produced in association with a jet. It is a generic possibility that the mediator of the interaction between DM and the nucleus will also be accessible at the Tevatron and the LHC. Therefore, collider search of the mediator can provide a more comprehensive probe of the dark matter and its interactions. In this article, to demonstrate the complementarity of these two approaches, we focus on the possibility of the mediator being a new $U(1)'$ gauge boson, which is probably the simplest model which allows a large direct detection cross section for a light dark matter candidate. We combine searches in the monojet+MET channel and dijet resonance search for the mediator. We find that for the mass of $Z'$ between 250 GeV and 4 TeV, resonance searches at the colliders provide stronger constraints on this model than the monojet+MET searches.Comment: 23 pages and 14 figure

LHC Discovery Potential for Non-Standard Higgs Bosons in the 3b Channel

In a variety of well motivated models, such as two Higgs Doublet Models (2HDMs) and the Minimal Supersymmetric Standard Model (MSSM), there are neutral Higgs bosons that have significantly enhanced couplings to b-quarks and tau leptons in comparison to those of the SM Higgs. These so called non-standard Higgs bosons could be copiously produced at the LHC in association with b quarks, and subsequently decay into b-quark pairs. However, this production channel suffers from large irreducible QCD backgrounds. We propose a new search strategy for non-standard neutral Higgs bosons at the 7 TeV LHC in the 3b's final state topology. We perform a simulation of the signal and backgrounds, using state of the art tools and methods for different sets of selection cuts, and conclude that neutral Higgs bosons with couplings to b-quarks of about 0.3 or larger, and masses up to 400 GeV, could be seen with a luminosity of 30 fb^{-1}. In the case of the MSSM we also discuss the complementarity between the 3b channel and the inclusive tau pair channel in exploring the supersymmetric parameter space.Comment: 14 pages, 3 figures, 4 tables, references added, published versio

Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

<p>Abstract</p> <p>Background</p> <p>In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.</p> <p>Methods</p> <p>Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.</p> <p>Results</p> <p>After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.</p> <p>Conclusion</p> <p>ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.</p

Colored Resonant Signals at the LHC: Largest Rate and Simplest Topology

We study the colored resonance production at the LHC in a most general approach. We classify the possible colored resonances based on group theory decomposition, and construct their effective interactions with light partons. The production cross section from annihilation of valence quarks or gluons may be on the order of 400 - 1000 pb at LHC energies for a mass of 1 TeV with nominal couplings, leading to the largest production rates for new physics at the TeV scale, and simplest event topology with dijet final states. We apply the new dijet data from the LHC experiments to put bounds on various possible colored resonant states. The current bounds range from 0.9 to 2.7 TeV. The formulation is readily applicable for future searches including other decay modes.Comment: 29 pages, 9 figures. References updated and additional K-factors include

Experimental measurement-based quantum computing beyond the cluster-state model

The paradigm of measurement-based quantum computation opens new experimental avenues to realize a quantum computer and deepens our understanding of quantum physics. Measurement-based quantum computation starts from a highly entangled universal resource state. For years, clusters states have been the only known universal resources. Surprisingly, a novel framework namely quantum computation in correlation space has opened new routes to implement measurement-based quantum computation based on quantum states possessing entanglement properties different from cluster states. Here we report an experimental demonstration of every building block of such a model. With a four-qubit and a six-qubit state as distinct from cluster states, we have realized a universal set of single-qubit rotations, two-qubit entangling gates and further Deutsch's algorithm. Besides being of fundamental interest, our experiment proves in-principle the feasibility of universal measurement-based quantum computation without using cluster states, which represents a new approach towards the realization of a quantum computer.Comment: 26 pages, final version, comments welcom

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure