Fluorescent labeling is widely used in biological and chemical analysis, and the drive for increased throughput is stretching multiplexing capabilities to the limit. The limiting factor in multiplexed analyses is the ability to subsequently deconvolute the signals. Consequently, alternative approaches for interpreting complex data sets are required to allow individual components to be identified. Here we have investigated the application of a novel approach to multiplexed analysis that does not rely on multivariate curve resolution to achieve signal deconvolution. The approach calculates a sample-specific confidence interval for a multivariate (partial least-squares regression (PLSR)) prediction, thereby enabling the estimation of the presence or absence of each fluorophore based on the total spectral signal. This approach could potentially be applied to any multiplexed measurement system and has the advantage over the current algorithm-based methods that the requirement for resolution of spectral peaks is not central to the method. Here, PLSR was used to obtain the concentrations for up to eight dye-labeled oligonucleotides at levels of (0.6-5.3) x 10(-6) M. The sample-specific prediction intervals show good discrimination for the presence/absence of seven of the eight labeled oligonucleotides with efficiencies ranging from approximately 91 to 100%.
<p>Paleoclimatic reconstructions have suggested a reduction inthe variability of the El Ni&#241;o Southern Oscillation (ENSO) during the mid-Holocene (MH). Model simulations have largely failed to capture thisreduction, potentially due to the inadequate representation of the Green Sahara.The presence of a vegetated Sahara has been shown to have significant impacts on both regional and remote climate but remains inadequately addressed in Paleoclimate Modelling Intercomparison Project / Coupled Model Intercomparison Project (PMIP/CMIP) boundary conditions. Specifically, the incorporation of a Green Sahara has been shown to impact ENSO variability through perturbations to the Walker Circulation. In this study, we evaluate the MH (6,000 years BP) ENSO signatures of simulations from four models, namely &#8212;EC-Earth 3.1, iCESM 1.2, University of Toronto version of CCSM4 and GISS Model E2.1-G. Two simulations are considered for each model&#8212;a standard PMIP simulation (MH<sub>PMIP</sub>) with the mid-Holocene orbital parameters and greenhouse gas concentrations with vegetation prescribed to preindustrial conditions, as well as a Green Sahara simulation (MH<sub>GS</sub>) which additionally incorporates factors such as enhanced vegetation, reduced dust, presence of lakes, and land and soil feedbacks. All models show a reduction in ENSO variability due to the incorporation of Green Sahara conditions. This variability is interpreted in the context of perturbations to the Walker Circulation, triggered by the strengthening of the West African Monsoon.</p>
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