49 research outputs found
Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort
Background: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts.
Methods: We applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres.
Results: Using both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.
Conclusion: This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets
Micromechanical Properties of Injection-Molded Starch–Wood Particle Composites
The micromechanical properties of injection molded starch–wood particle composites were investigated as a function of particle content and humidity conditions.
The composite materials were characterized by scanning electron microscopy and X-ray diffraction methods. The microhardness
of the composites was shown to increase notably with the concentration of the wood particles. In addition,creep behavior under the indenter and temperature dependence
were evaluated in terms of the independent contribution of the starch matrix and the wood microparticles to the hardness value. The influence of drying time on the density
and weight uptake of the injection-molded composites was highlighted. The results revealed the role of the mechanism of water evaporation, showing that the dependence of water uptake and temperature was greater for the starch–wood composites than for the pure starch sample. Experiments performed during the drying process at 70°C indicated that
the wood in the starch composites did not prevent water loss from the samples.Peer reviewe
Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review
Evaluating spawning migration patterns and predicting spawning success of shovelnose sturgeon in the Lower Missouri River
Approaches using telemetry, precise reproductive assessments, and surgically implanted data storage tags (DSTs) were used in combination with novel applications of analytical techniques for fish movement studies to describe patterns in migratory behavior and predict spawning success of gravid shovelnose sturgeon. From 2004 to 2007, over 300 gravid female shovelnose sturgeon (Scaphirhynchus platorynchus) from the Lower Missouri River, that were expected to spawn in the year they were collected, were surgically implanted with transmitters and archival DSTs. Functional cluster modeling of telemetry data from the spawning season suggested two common migration patterns of gravid female shovelnose sturgeon. Fish implanted from 958 to 1181 river kilometer (rkm) from the mouth of the Missouri River (or northern portion of the Lower Missouri River within 354 rkm of the lowest Missouri River dam at rkm 1305) had one migration pattern. Of fish implanted from 209 to 402 rkm from the mouth of the Missouri River (or southern portion of the Lower Missouri River), half demonstrated a movement pattern similar to the northern fish while the other half demonstrated a migration pattern that covered more of the river. There was no apparent difference in migration patterns between successful and unsuccessful spawners. Multiple hypotheses exist to explain differences in migratory patterns among fish from different river reaches. Additional work is required to determine if observed differences are due to multiple adapted strategies, environmental alteration, and ⁄ or initial tagging date. Hierarchical Bayesian modeling of DST data indicated that variation in depth usage patterns was consistently different between successful and unsuccessful spawners, as indicated by differences in likelihood of switching between high and low variability states. Analyses of DST data, and data collected at capture, were sufficient to predict 8 of 10 non-spawners ⁄ incomplete spawners and all 30 spawners in the absence of telemetry location data. Together, the results of these two separate analyses suggest that caution is necessary in extrapolating spawning success from broad-scale movement data alone. More direct measures of spawning success may be necessary to precisely determine spawning success and to evaluate the effects of management actions
Incidental Pulmonary Emboli in Oncology Patients: Prevalence, CT Evaluation, and Natural History
Pretreatment positron emission tomography (PET) scan standardized uptake value (SUV) as a prognostic variable for overall survival in limited-stage small cell lung cancer (L-SCLC).
Assessing mutual funds’ corporate social responsibility: a multistakeholder-AHP based methodology
Mormon and Irish Landscapes on Beaver Island, Lake Michigan – niche construction and socio-ecological inheritance in the nineteenth century
Spatio-temporal assimilation of modelled catchment loads with monitoring data in the Great Barrier Reef
Soil erosion and sediment transport into waterways and the ocean can adversely affect water clarity, leading to the deterioration of marine ecosystems such as the iconic Great Barrier Reef (GBR) in Australia. Quantifying a sediment load and its associated uncertainty is an important task in delineating how changes in management practices can contribute to improvements in water quality, and therefore continued sustainability of the GBR. However, monitoring data are spatially (and often temporally) sparse, making load estimation complicated, particularly when there are lengthy periods between sampling or during peak flow periods of major events when samples cannot be safely taken.\ud
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We develop a spatio-temporal statistical model that is mechanistically motivated by a process-based deterministic model called Dynamic SedNet. The model is developed within a Bayesian hierarchical modelling framework that uses dimension reduction to accommodate seasonal and spatial patterns to assimilate monitored sediment concentration and flow data with output from Dynamic SedNet. The approach is applied in the Upper Burdekin catchment in Queensland, Australia, where we obtain daily estimates of sediment concentrations, stream discharge volumes and sediment loads at 411 spatial locations across 20 years. Our approach provides a method for assimilating both monitoring data and modelled output, providing a statistically rigorous method for quantifying uncertainty through space and time that was previously unavailable through process-based models