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Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial “patches”. Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza. Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols.

We present and characterize a multi-host epidemic model of Rift Valley fever (RVF) virus in East Africa with geographic spread on a network, rule-based mitigation measures, and mosquito infection and population dynamics. Susceptible populations are depleted by disease and vaccination and are replenished with the birth of new animals. We observe that the severity of the epidemics is strongly correlated with the duration of the rainy season and that even severe epidemics are abruptly terminated when the rain stops. Because naturally acquired herd immunity is established, total mortality across 25 years is relatively insensitive to many mitigation approaches. Strong reductions in cattle mortality are expected, however, with sufficient reduction in population densities of either vectors or susceptible (ie. unvaccinated) hosts. A better understanding of RVF epidemiology would result from serology surveys to quantify the importance of herd immunity in epidemic control, and sequencing of virus from representative animals to quantify the realative importance of transportation and local reservoirs in nucleating yearly epidemics. Our results suggest that an effective multi-layered mitigation strategy would include vector control, movement control, and vaccination of young animals yearly, even in the absence of expected rainfall.

The relative contributions of HLA alleles and T-cell receptors (TCRs) to the prevention of mutational viral escape are unclear. Here, we examined human immunodeficiency virus type 1 (HIV-1)-specific CD8 + T-cell responses restricted by two closely related HLA class I alleles, B*5701 and B*5703, that differ by two amino acids but are both associated with a dominant response to the same HIV-1 Gag epitope KF11 (KAFSPEVIPMF). When this epitope is presented by HLA-B*5701, it induces a TCR repertoire that is highly conserved among individuals, cross-recognizes viral epitope variants, and is rarely associated with mutational escape. In contrast, KF11 presented by HLA-B*5703 induces an entirely different, more heterogeneous TCR β-chain repertoire that fails to recognize specific KF11 escape variants which frequently arise in clade C-infected HLA-B*5703 + individuals. These data show the influence of HLA allele subtypes on TCR selection and indicate that extensive TCR diversity is not a prerequisite to prevention of allowable viral mutations.

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

We have examined cobalt based valence tautomer molecules such as Co(SQ)$_2$(phen) using density functional theory (DFT) and variational configuration interaction (VCI) approaches based upon a model Hamiltonian. Our DFT results extend earlier work by finding a reduced total energy gap (order 0.6 eV) between high temperature and low temperature states when we fully relax the coordinates (relative to experimental ones). Futhermore we demonstrate that the charge transfer picture based upon formal valence arguments succeeds qualitatively while failing quantitatively due to strong covalency between the Co 3$d$ orbitals and ligand $p$ orbitals. With the VCI approach, we argue that the high temperature, high spin phase is strongly mixed valent, with about 30 % admixture of Co(III) into the predominantly Co(II) ground state. We confirm this mixed valence through a fit to the XANES spectra. Moreover, the strong electron correlations of the mixed valent phase provide an energy lowering of about 0.2-0.3 eV of the high temperature phase relative to the low temperature one. Finally, we use the domain model to account for the extraordinarily large entropy and enthalpy values associated with the transition., 10 pages, 4 figures, submitted to J. Chem. Phys

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Human leukocyte antigen (HLA)-B27-positive subjects are uncommon in their ability to control infection with human immunodeficiency virus type 1 (HIV-1). However, late viral escape from a narrowly directed immunodominant Gag-specific CD8+T-lymphocyte (CTL) response has been linked to AIDS progression in these individuals. Identifying the mechanism of the immune-mediated control may provide critical insights into HIV-1 vaccine development. Here, we illustrate that the CTL escape mutation R264K in the HLA-B27-restricted KK10 epitope in the capsid resulted in a significant defect in viral replication in vitro. The R264K variant was impaired in generating late reverse transcription products, indicating that replication was blocked at a postentry step. Notably, the R264K mutation was associated in vivo with the development of a rare secondary mutation, S173A, which restored viral replication in vitro. Furthermore, infectivity of the R264K variant was rescued by the addition of cyclosporine A or infection of a cyclophilin A-deficient cell line. These data demonstrate a severe functional defect imposed by the R264K mutation during an early step in viral replication that is likely due to the inability of this variant to replicate efficiently in the presence of normal levels of cyclophilin A. We conclude that the impact of the R264K substitution on capsid structure constrains viral escape and enables long-term maintenance of the dominant CTL response against B27-KK10, providing an explanation for the protective effect of HLA-B27 during HIV infection.

Atomic motions and energetics for a phosphate transfer reaction catalyzed by the cAMP-dependent protein kinase (PKA) are calculated by plane-wave density functional theory, starting from structures of proteins crystallized in both the reactant conformation (RC) and the transition-state conformation (TC). In the TC, we calculate that the reactants and products are nearly isoenergetic with a 0.2 eV barrier; while phosphate transfer is unfavorable by over 1.2 eV in the RC, with an even higher barrier. With the protein in the TC, the motions involved in reaction are small, with only P$_\gamma$ and the catalytic proton moving more than 0.5 \AA. Examination of the structures reveals that in the RC the active site cleft is not completely closed and there is insufficient space for the phosphorylated serine residue in the product state. Together, these observations imply that the phosphate transfer reaction occurs rapidly and reversibly in a particular conformation of the protein, and that the reaction can be gated by changes of a few tenths of an \AA in the catalytic site., Comment: revtex4, 7 pages, 4 figures, to be submitted to Science

We present a model intended for rapid sampling of ground and excited state potential energy surfaces for first-row transition metal active sites. The method is computationally inexpensive and is suited for dynamics simulations where (1) adiabatic states are required "on-the-fly" and (2) the primary source of the electronic coupling between the diabatic states is the perturbative spin-orbit interaction among the 3d electrons. The model Hamiltonian we develop is a variant of the Anderson impurity model and achieves efficiency through a physically motivated basis set reduction based on the large value of the d-d Coulomb interaction U_{d} and a Lanczos matrix diagonalization routine to solve for eigenvalues. The model parameters are constrained by fits to the partial density of states (PDOS) obtained from ab initio density functional theory calculations. For a particular application of our model we focus on electron-transfer occuring between cobalt ions solvated by ammonium, incorporating configuration interaction between multiplet states for both metal ions. We demonstrate the capability of the method to efficiently calculate adiabatic potential energy surfaces and the electronic coupling factor we have calculated compares well to previous calculations and experiment., revtex4, 11 pages, 8 figures, submitted to J. Chem. Phys

We have reexamined the thermally induced Co(NH_3)_6^{2+/3+} [Co(II/III)] redox reaction using the first principles density-functional-theory method, semiclassical Marcus theory, and known charge transfer parameters. We confirm a previously suggested mechanism involving excited state (^2E_g) of Co(II) which becomes lower than the ground state (^4T_1g) in the transition state region. This lowers the transition state barrier considerably by about 6.9 kcal/mol and leads to a spin-allowed and adiabatic electron exchange process. Our calculations are consistent with previous experimental results regarding the spin-excitation energy (^3T_1g) of Co(III), and the fact that an optical absorption peak (^2E_g) of the Co(II) species could not be found experimentally. Our rate is of order 6 10^{-3} 1/Ms and hence 2 orders of magnitude faster than determined previously by experiments., Comment: 10 pages, 5 figures, 4 tables; submitted to J.Chem.Phys

Background and purpose The ability to track changes in gene expression following viral infection is paramount to understanding viral pathogenesis. This study was undertaken to evaluate the nCounter, a high throughput digital gene expression system, as a means to better understand West Nile virus (WNV) dissemination and the inflammatory response against WNV in the outbred Swiss Webster (SW) mouse model over the course of infection. Methodology The nCounter Mouse Inflammation gene expression kit containing 179 inflammation related genes was used to analyze gene expression changes in multiple tissues over a nine day course of infection in SW mice following intraperitoneal injection with WNV. Protein expression levels for a subset of these cytokine/chemokine genes were determined using a multiplex protein detection system (BioPlex) and comparisons of protein/RNA expression levels made. Results Expression analysis of spleen, lung, liver, kidney and brain of SW mice infected with WNV revealed that Cxcl10 and Il12b are differentially expressed in all tissues tested except kidney. Data stratification of positively confirmed infected (WNV (+)) versus non-infected (WNV (−) tissues allowed differentiation of the systemic inflammatory gene response from tissue-specific responses arising from WNV infection. Significant (p, Author Summary The nCounter, a high throughput digital molecular detection and quantitation system, was used to define the inflammatory response of SW mice to WNV infection following intraperitoneal injection in multiple tissues over a 9 day course of viral infection. Data analysis identified expression profiles common and unique to major tissues. Complementary protein suspension array analysis of a subset of genes demonstrated the expression kinetics of RNA and protein expression compared to infectious virus production. Data stratification of WNV (+) versus WNV (−) tissues allowed differentiation of the systemic inflammatory gene response versus tissue-specific responses due to infection with WNV over time. The results generated from this study will allow for further hypothesis generation and testing.

With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.

Allosteric (conformation changing) proteins with transition metal atoms are at the heart of much important biological function (e.g., myoglobin hemoglobin used for storing and transporting oxygen in the bloodstream). In the case of myoglobin and hemoglobin, oxygen ligation to the iron center induces a spin crossover (high to low) coupled to a structural change; apart from the role of Hunds' exchange in the spin crossover, electron interaction effects have been ignored. We argue that the spin crossover/structure change observed in the similarly structured but far simpler cobalt valence tautomer molecules1 necessitates an inclusion of underscreened Kondo like correlations for a complete description of the energetics of the transition and dynamics, e.g., for x-ray absorption data. We carry this study out with Varma-Yafet-Gunnarsson-Schonhammer wave functions, which, in chemistry language, are basis set restricted configuration interaction in character. We briefly review the applicability of such wave functions to the description of the putative Kondo molecules cerocene (Ce[(CH)5]2) and ytterbocene bipyridine (Yb[(CH)5]2(bipy)) and to the problem of electron transfer in biological molecules and organic conductors, where anomalous long range tunneling may occur. Research supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Materials Research.

For the past decade, the Food and Agriculture Organization of the United Nations has been working toward eradicating rinderpest through vaccination and intense surveillance by 2012. Because of the potential severity of a rinderpest epidemic, it is prudent to prepare for an unexpected outbreak in animal populations. There is no immunity to the disease among the livestock or wildlife in the United States (US). If rinderpest were to emerge in the US, the loss in livestock could be devastating. We predict the potential spread of rinderpest using a two-stage model for the spread of a multi-host infectious disease among agricultural animals in the US. The model incorporates large-scale interactions among US counties and the small-scale dynamics of disease spread within a county. The model epidemic was seeded in 16 locations and there was a strong dependence of the overall epidemic size on the starting location. The epidemics were classified according to overall size into small epidemics of 100 to 300 animals (failed epidemics), epidemics infecting 3 000 to 30 000 animals (medium epidemics), and the large epidemics infecting around one million beef cattle. The size of the rinderpest epidemics were directly related to the origin of the disease and whether or not the disease moved into certain key counties in high-livestock-density areas of the US. The epidemic size also depended upon response time and effectiveness of movement controls.