A simulation framework to investigate in vitro viral infection dynamics

Virus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24 h post infection. Using a simulated annealing algorithm we tune free parameters with data from SARSCoV infection of cultured lung epithelial cells. We also interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles

Methods for attributing land-use emissions to products

Roughly one-third of anthropogenic GHG emissions are caused by agricultural and forestry activities and land-use change (collectively, land-use emissions'). Understanding the ultimate drivers of these emissions requires attributing emissions to specific land-use activities and products. Although quantities of land-use emissions are matters of fact, the methodological choices and assumptions required to attribute those emissions to activities and products depend on research goals and data availability. In this review, we explore several possible accounting methods. Our results highlight the sensitivity of accounting to temporal distributions of emissions and the consequences of replacing spatially-explicit data with aggregate proxies such as production or harvested area data. Different accounting options emphasize different causes of land-use emissions (e.g., proximate or indirect drivers of deforestation). To support public policies that effectively balance competing objectives, analysts should carefully consider and communicate implications of accounting choices

Measuring enteric methane emissions from individual ruminant animals in their natural environment

Ruminant livestock are an important source of meat, milk, fiber, and labor for humans. The process by which ruminants digest plant material through rumen fermentation into useful product results in the loss of energy in the form of methane gas from consumed organic matter. The animal removes the methane building up in its rumen by repeated eructations of gas through its mouth and nostrils. Ruminant livestock are a notable source of atmospheric methane, with an estimated 17% of global enteric methane emissions from livestock. Historically, enteric methane was seen as an inefficiency in production and wasted dietary energy. This is still the case, but now methane is seen more as a pollutant and potent greenhouse gas. The gold standard method for measuring methane production from individual animals is a respiration chamber, which is used for metabolic studies. This approach to quantifying individual animal emissions has been used in research for over 100 years; however, it is not suitable for monitoring large numbers of animals in their natural environment on commercial farms. In recent years, several more mobile monitoring systems discussed here have been developed for direct measurement of enteric methane emissions from individual animals. Several factors (diet composition, rumen microbial community, and their relationship with morphology and physiology of the host animal) drive enteric methane production in ruminant populations. A reliable method for monitoring individual animal emissions in large populations would allow (1) genetic selection for low emitters, (2) benchmarking of farms, and (3) more accurate national inventory accounting

Locating the Binding Sites of Pb(II) Ion with Human and Bovine Serum Albumins

Lead is a potent environmental toxin that has accumulated above its natural level as a result of human activity. Pb cation shows major affinity towards protein complexation and it has been used as modulator of protein-membrane interactions. We located the binding sites of Pb(II) with human serum (HSA) and bovine serum albumins (BSA) at physiological conditions, using constant protein concentration and various Pb contents. FTIR, UV-visible, CD, fluorescence and X-ray photoelectron spectroscopic (XPS) methods were used to analyse Pb binding sites, the binding constant and the effect of metal ion complexation on HSA and BSA stability and conformations. Structural analysis showed that Pb binds strongly to HSA and BSA via hydrophilic contacts with overall binding constants of KPb-HSA = 8.2 (±0.8)×104 M−1 and KPb-BSA = 7.5 (±0.7)×104 M−1. The number of bound Pb cation per protein is 0.7 per HSA and BSA complexes. XPS located the binding sites of Pb cation with protein N and O atoms. Pb complexation alters protein conformation by a major reduction of α-helix from 57% (free HSA) to 48% (metal-complex) and 63% (free BSA) to 52% (metal-complex) inducing a partial protein destabilization

Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model

<p>Abstract</p> <p>Background</p> <p>Developing a quantitative understanding of viral kinetics is useful for determining the pathogenesis and transmissibility of the virus, predicting the course of disease, and evaluating the effects of antiviral therapy. The availability of data in clinical, animal, and cell culture studies, however, has been quite limited. Many studies of virus infection kinetics have been based solely on measures of total or infectious virus count. Here, we introduce a new mathematical model which tracks both infectious and total viral load, as well as the fraction of infected and uninfected cells within a cell culture, and apply it to analyze time-course data of an SHIV infection <it>in vitro</it>.</p> <p>Results</p> <p>We infected HSC-F cells with SHIV-KS661 and measured the concentration of Nef<it>-</it>negative (target) and Nef<it>-</it>positive (infected) HSC-F cells, the total viral load, and the infectious viral load daily for nine days. The experiments were repeated at four different MOIs, and the model was fitted to the full dataset simultaneously. Our analysis allowed us to extract an infected cell half-life of 14.1 h, a half-life of SHIV-KS661 infectiousness of 17.9 h, a virus burst size of 22.1 thousand RNA copies or 0.19 TCID₅₀, and a basic reproductive number of 62.8. Furthermore, we calculated that SHIV-KS661 virus-infected cells produce at least 1 infectious virion for every 350 virions produced.</p> <p>Conclusions</p> <p>Our method, combining <it>in vitro </it>experiments and a mathematical model, provides detailed quantitative insights into the kinetics of the SHIV infection which could be used to significantly improve the understanding of SHIV and HIV-1 pathogenesis. The method could also be applied to other viral infections and used to improve the <it>in vitro </it>determination of the effect and efficacy of antiviral compounds.</p

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente