Researchers at UCLA have discovered that the levels of interleukin-8 (IL-8) protein in the saliva of healthy individuals and patients with oropharyngeal squamous cell carcinoma (OSCC) are 30 pM and 86 pM, respectively. In this study, we present the development of the first immunoassay for the quantification of picomolar IL-8 concentrations in human saliva using Biacore surface plasmon resonance (SPR) in a microfluidic channel. A sandwich assay using two monoclonal antibodies, which recognize different epitopes on the antigen (IL-8), was used. Only 13 minutes were required to determine the quantity of pure IL-8 added to just 100 microL of either buffer or saliva-based samples. The limit of detection (LOD) of this immunoassay in buffer was 2.5 pM, and the precision of the response for each concentration was <3% of the coefficient of variation. When first analyzing the saliva supernatants, non-specific binding to the surface was observed. By adding carboxymethyl dextran sodium salt (10 mg mL(-1)) to compete with the surface dextran and primary antibody for non-specific interactions, the signal to noise ratio was greatly improved. The LOD of this immunoassay in saliva was 184 pM. A minimum concentration of 250 pM of exogenous IL-8 could then be consistently detected in a salivary environment. The precision of the response for each IL-8 concentration tested was <7% of the coefficient of variation. Diagnostic sensitivity for oral cancer can be achieved by pre-concentrating the saliva samples 10 fold prior to SPR analysis, making the target levels of IL-8 300 pM for healthy individuals and 860 pM for oral cancer patients.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. However, the influence of different experimental treatments on those predictions depends on the exact methods and techniques used for data assimilation. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation of Pine Plantation Ecosystem Research, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the Southeastern U.S. to constrain parameters in a modified version of the 3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) concentration, water, and nutrients, along with non-experimental studies that spanned environmental gradients across an 8.6&amp;#8201;&amp;#215;&amp;#8201;10&lt;sup&gt;5&lt;/sup&gt;&amp;#8201;km&lt;sup&gt;2&lt;/sup&gt; region. We optimized regionally representative posterior distributions for the most sensitive model parameters, which dependably predicted data from plots withheld from the data assimilation. The posterior distributions of parameters associated with ecosystem responses to CO&lt;sub&gt;2&lt;/sub&gt;, precipitation, and nutrient addition, along with the corresponding regional changes in production associated with nutrient fertilization and drought, depended on how the experimental data were assimilated. In particular, assimilating nutrient addition experiments reduced the predicted sensitivity to nutrient fertilization while assimilated water manipulation experiments increased the sensitivity to drought. Further, it was necessary to assimilate data from the CO&lt;sub&gt;2&lt;/sub&gt; experimental enrichment site before other studies to constrain the parameters associated with the influence of CO&lt;sub&gt;2&lt;/sub&gt; on canopy photosynthesis. The ambient CO&lt;sub&gt;2&lt;/sub&gt; plots were numerous and had a large contribution to the cost function compared to the low number of elevated CO&lt;sub&gt;2&lt;/sub&gt; plots (289 ambient vs. 5 elevated plots). Overall, we demonstrated how three decades of research in southeastern U.S. planted pine forests can be used to develop data assimilation techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters. This approach allows for future predictions to be consistent with a rich history of ecosystem research across a region.&lt;/p&gt;

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