Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in Northern Tanzania as a case-study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics

Next-to-Leading Order Cross Sections for Tagged Reactions

We extend the phase space slicing method of Giele, Glover and Kosower for performing next-to-leading order jet cross section calculations in two important ways: we show how to include fragmentation functions and how to include massive particles. These extensions allow the application of this method to not just jet cross sections but also to cross sections in which a particular final state particle, including a $D$ or $B$-meson, is tagged.Comment: 36 pages, Latex Small corrections to text. To appear in Phys. Rev.

Twist-2 Heavy Flavor Contributions to the Structure Function g2(x,Q2)g_2(x,Q^2)

The twist--2 heavy flavor contributions to the polarized structure function $g_2(x,Q^2)$ are calculated. We show that this part of $g_2(x,Q^2)$ is related to the heavy flavor contribution to $g_1(x,Q^2)$ by the Wandzura--Wilczek relation to all orders in the strong coupling constant. Numerical results are presented.Comment: 17 pages LATEX, 1 style files, 4 figure

Scheme Independence of g1p(x,Q2)g_1^p (x, Q^2)

We work with two general factorization schemes in order to explore the consequences of imposing scheme independence on $g_1^p (x, Q^2)$. We see that although the light quark sector is indifferent to the choice of a particular scheme, the extension of the calculations to the heavy quark sector indicates that a scheme like the $\bar{MS}$ is preferable.Comment: 11 pages, 2 figures. To appear in the Brief Reports of Phys. Rev.

A New 5-Flavour LO Analysis and Parametrization of Parton Distributions in the Real Photon

New, radiatively generated, LO quark (u,d,s,c,b) and gluon densities in a real, unpolarized photon are presented. We perform a global 3-parameter fit, based on LO DGLAP evolution equations, to all available data for the structure function F2^gamma(x,Q^2). We adopt a new theoretical approach called ACOT(chi), originally introduced for the proton, to deal with the heavy-quark thresholds. This defines our basic model (CJKL model), which gives a very good description of the experimental data on F2^gamma(x,Q^2), for both Q^2 and x dependences. For comparison we perform a standard fit using the Fixed Flavour-Number Scheme (FFNS_CJKL model), updated with respect to the previous fits of this type. We show the superiority of the CJKL fit over the FFNS_CJKL one and other LO fits to the F2^gamma(x,Q^2) data. The CJKL model gives also the best description of the LEP data on the Q^2 dependence of the F2^gamma, averaged over various x-regions, and the F_2,c^gamma, which were not used directly in the fit. Finally, a simple analytic parametrization of the resulting parton densities obtained with the CJKL model is given.Comment: 43 pages, RevTeX4 using axodraw style, 3 tex and 12 postscript figures, version submitted to Phys. Rev. D, small text changes, one reference added, FORTRAN program available at http://www.fuw.edu.pl/~pjank/param.html and at http://www-zeuthen.desy.de/~alorca/id4.htm

Facilitating functional annotation of chicken microarray data

<p>Abstract</p> <p>Background</p> <p>Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information.</p> <p>Results</p> <p>We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (<it>AGOM</it>) tool to help researchers to quickly retrieve corresponding functional information for their dataset.</p> <p>Conclusion</p> <p>Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using <it>AGOM </it>tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.</p

Hard-scattering factorization with heavy quarks: A general treatment

A detailed proof of hard scattering factorization is given with the inclusion of heavy quark masses. Although the proof is explicitly given for deep-inelastic scattering, the methods apply more generally The power-suppressed corrections to the factorization formula are uniformly suppressed by a power of \Lambda/Q, independently of the size of heavy quark masses, M, relative to Q.Comment: 52 pages. Version as published plus correction of misprint in Eq. (45

b-Initiated processes at the LHC: a reappraisal

Several key processes at the LHC in the standard model and beyond that involve $b$ quarks, such as single-top, Higgs, and weak vector boson associated production, can be described in QCD either in a 4-flavor or 5-flavor scheme. In the former, $b$ quarks appear only in the final state and are typically considered massive. In 5-flavor schemes, calculations include $b$ quarks in the initial state, are simpler and allow the resummation of possibly large initial state logarithms of the type $\log \frac{{\cal Q}^2}{m_b^2}$ into the $b$ parton distribution function (PDF), ${\cal Q}$ being the typical scale of the hard process. In this work we critically reconsider the rationale for using 5-flavor improved schemes at the LHC. Our motivation stems from the observation that the effects of initial state logs are rarely very large in hadron collisions: 4-flavor computations are pertubatively well behaved and a substantial agreement between predictions in the two schemes is found. We identify two distinct reasons that explain this behaviour, i.e., the resummation of the initial state logarithms into the $b$-PDF is relevant only at large Bjorken $x$ and the possibly large ratios ${\cal Q}^2/m_b^2$'s are always accompanied by universal phase space suppression factors. Our study paves the way to using both schemes for the same process so to exploit their complementary advantages for different observables, such as employing a 5-flavor scheme to accurately predict the total cross section at NNLO and the corresponding 4-flavor computation at NLO for fully exclusive studies.Comment: Fixed typo in Eq. (A.10) and few typos in Eq. (C.2) and (C.3

The fully differential single-top-quark cross section in next-to-leading order QCD

We present a new next-to-leading order calculation for fully differential single-top-quark final states. The calculation is performed using phase space slicing and dipole subtraction methods. The results of the methods are found to be in agreement. The dipole subtraction method calculation retains the full spin dependence of the final state particles. We show a few numerical results to illustrate the utility and consistency of the resulting computer implementations.Comment: 37 pages, latex, 2 ps figure

Genotype-Dependent Tumor Regression in Marek’s Disease Mediated at the Level of Tumor Immunity

Marek’s disease (MD) of chickens is a unique natural model of Hodgkin’s and Non Hodgkin’s lymphomas in which the neoplastically-transformed cells over-express CD30 (CD30hi) antigen. All chicken genotypes can be infected with MD virus and develop microscopic lymphomas. From 21 days post infection (dpi) microscopic lymphomas regress in resistant chickens but, in contrast, they progress to gross lymphomas in susceptible chickens. Here we test our hypothesis that in resistant chickens at 21 dpi the tissue microenvironment is pro T-helper (Th)-1 and compatible with cytotoxic T lymphocyte (CTL) immunity but in susceptible lines it is pro Th-2 or pro T-regulatory (T-reg) and antagonistic to CTL immunity. We used the B2, non-MHC-associated, MD resistance/susceptibility system (line [L]61/line [L]72) and quantified the levels of key mRNAs that can be used to define Th-1 (IL-2, IL-12, IL-18, IFNγ), Th-2 (IL-4, IL-10) and T-reg (TGFβ, GPR-83, CTLA-4, SMAD-7) lymphocyte phenotypes. We measured gene expression in both whole tissues (represents tissue microenvironment and tumor microenvironment) and in the lymphoma lesions (tumor microenvironment) themselves. Gene ontology-based modeling of our results shows that the dominant phenotype in whole tissue as well as in microscopic lymphoma lesions, is pro T-reg in both L61 and L72 but a minor pro Th-1 and anti Th-2 tissue microenvironment exists in L61 whereas there is an anti Th-1 and pro Th-2 tissue microenvironment in L72. The tumor microenvironment per se is pro T-reg, anti Th-1 and pro Th-2 in both L61 and L72. Together our data suggests that the neoplastic transformation is essentially the same in both L61 and L72 and that resistance/susceptibility is mediated at the level of tumor immunity in the tissues