Prompt atmospheric neutrino fluxes: perturbative QCD models and nuclear effects

We evaluate the prompt atmospheric neutrino flux at high energies using three different frameworks for calculating the heavy quark production cross section in QCD: NLO perturbative QCD, $k_T$ factorization including low-$x$ resummation, and the dipole model including parton saturation. We use QCD parameters, the value for the charm quark mass and the range for the factorization and renormalization scales that provide the best description of the total charm cross section measured at fixed target experiments, at RHIC and at LHC. Using these parameters we calculate differential cross sections for charm and bottom production and compare with the latest data on forward charm meson production from LHCb at $7$ TeV and at $13$ TeV, finding good agreement with the data. In addition, we investigate the role of nuclear shadowing by including nuclear parton distribution functions (PDF) for the target air nucleus using two different nuclear PDF schemes. Depending on the scheme used, we find the reduction of the flux due to nuclear effects varies from $10\%$ to $50 \%$ at the highest energies. Finally, we compare our results with the IceCube limit on the prompt neutrino flux, which is already providing valuable information about some of the QCD models.Comment: 61 pages, 25 figures, 11 table

Scaling Laws in Human Language

Zipf's law on word frequency is observed in English, French, Spanish, Italian, and so on, yet it does not hold for Chinese, Japanese or Korean characters. A model for writing process is proposed to explain the above difference, which takes into account the effects of finite vocabulary size. Experiments, simulations and analytical solution agree well with each other. The results show that the frequency distribution follows a power law with exponent being equal to 1, at which the corresponding Zipf's exponent diverges. Actually, the distribution obeys exponential form in the Zipf's plot. Deviating from the Heaps' law, the number of distinct words grows with the text length in three stages: It grows linearly in the beginning, then turns to a logarithmical form, and eventually saturates. This work refines previous understanding about Zipf's law and Heaps' law in language systems.Comment: 6 pages, 4 figure

HGF Mediates the Anti-inflammatory Effects of PRP on Injured Tendons

Platelet-rich plasma (PRP) containing hepatocyte growth factor (HGF) and other growth factors are widely used in orthopaedic/sports medicine to repair injured tendons. While PRP treatment is reported to decrease pain in patients with tendon injury, the mechanism of this effect is not clear. Tendon pain is often associated with tendon inflammation, and HGF is known to protect tissues from inflammatory damages. Therefore, we hypothesized that HGF in PRP causes the anti-inflammatory effects. To test this hypothesis, we performed in vitro experiments on rabbit tendon cells and in vivo experiments on a mouse Achilles tendon injury model. We found that addition of PRP or HGF decreased gene expression of COX-1, COX-2, and mPGES-1, induced by the treatment of tendon cells in vitro with IL-1β. Further, the treatment of tendon cell cultures with HGF antibodies reduced the suppressive effects of PRP or HGF on IL-1β-induced COX-1, COX-2, and mPGES-1 gene expressions. Treatment with PRP or HGF almost completely blocked the cellular production of PGE2 and the expression of COX proteins. Finally, injection of PRP or HGF into wounded mouse Achilles tendons in vivo decreased PGE2 production in the tendinous tissues. Injection of platelet-poor plasma (PPP) however, did not reduce PGE2 levels in the wounded tendons, but the injection of HGF antibody inhibited the effects of PRP and HGF. Further, injection of PRP or HGF also decreased COX-1 and COX-2 proteins. These results indicate that PRP exerts anti-inflammatory effects on injured tendons through HGF. This study provides basic scientific evidence to support the use of PRP to treat injured tendons because PRP can reduce inflammation and thereby reduce the associated pain caused by high levels of PGE2. © 2013 Zhang et al

A measure of individual role in collective dynamics

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.Comment: accepted for publication in Scientific Report

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Transparent SiON/Ag/SiON multilayer passivation grown on a flexible polyethersulfone substrate using a continuous roll-to-roll sputtering system

We have investigated the characteristics of a silicon oxynitride/silver/silicon oxynitride [SiON/Ag/SiON] multilayer passivation grown using a specially designed roll-to-roll [R2R] sputtering system on a flexible polyethersulfone substrate. Optical, structural, and surface properties of the R2R grown SiON/Ag/SiON multilayer were investigated as a function of the SiON thickness at a constant Ag thickness of 12 nm. The flexible SiON/Ag/SiON multilayer has a high optical transmittance of 87.7% at optimized conditions due to the antireflection and surface plasmon effects in the oxide-metal-oxide structure. The water vapor transmission rate of the SiON/Ag/SiON multilayer is 0.031 g/m2 day at an optimized SiON thickness of 110 nm. This indicates that R2R grown SiON/Ag/SiON is a promising thin-film passivation for flexible organic light-emitting diodes and flexible organic photovoltaics due to its simple and low-temperature process

Narrowband Biphotons: Generation, Manipulation, and Applications

In this chapter, we review recent advances in generating narrowband biphotons with long coherence time using spontaneous parametric interaction in monolithic cavity with cluster effect as well as in cold atoms with electromagnetically induced transparency. Engineering and manipulating the temporal waveforms of these long biphotons provide efficient means for controlling light-matter quantum interaction at the single-photon level. We also review recent experiments using temporally long biphotons and single photons.Comment: to appear as a book chapter in a compilation "Engineering the Atom-Photon Interaction" published by Springer in 2015, edited by A. Predojevic and M. W. Mitchel

Metabolic pathway alignment between species using a comprehensive and flexible similarity measure

Comparative analysis of metabolic networks in multiple species yields important information on their evolution, and has great practical value in metabolic engineering, human disease analysis, drug design etc. In this work, we aim to systematically search for conserved pathways in two species, quantify their similarities, and focus on the variations between themElectrical Engineering, Mathematics and Computer Scienc