Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Updating the ELISA standard curve fitting process to reduce uncertainty in estimated microcystin concentrations.

ABSTRACT: This study is aimed at exploring the optimal ELISA standard curve fitting process for reducing measurement uncertainty. Using an ELISA kit for measuring cyanobacterial toxin (microcystin), we show that uncertainty associated with the estimated microcystin concentrations can be reduced by defining the standard curve as a four-parameter logistic function on the natural log concentration scale, instead of the current approach of defining the curve on the concentration scale. The model comparison method is outlined in this paper, allowing it to be transferable to test different statistical models for other ELISA test kits.

SUBMITTER: Nummer SA

PROVIDER: S-EPMC6050442 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Updating the ELISA standard curve fitting process to reduce uncertainty in estimated microcystin concentrations.

Nummer Stephanie A SA Weeden Alexandra J AJ Shaw Chloe C Snyder Brenda K BK Bridgeman Thomas B TB Qian Song S SS

MethodsX 20180412

This study is aimed at exploring the optimal ELISA standard curve fitting process for reducing measurement uncertainty. Using an ELISA kit for measuring cyanobacterial toxin (microcystin), we show that uncertainty associated with the estimated microcystin concentrations can be reduced by defining the standard curve as a four-parameter logistic function on the natural log concentration scale, instead of the current approach of defining the curve on the concentration scale. The model comparison me ...[more]

PMID: 30023312

Similar Datasets

Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses.

Project description:Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P-E). Here, we compute P-E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P-E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P-E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P-E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making.

| S-EPMC4937427 | biostudies-literature

Baroreflex Curve Fitting Using a WYSIWYG Boltzmann Sigmoidal Equation.

Project description:Arterial baroreflex assessment using vasoactive substances enables investigators to collect data pairs over a wide range of blood pressures and reflex reactions. These data pairs relate intervals between heartbeats or sympathetic neural activity to blood pressure values. In an X-Y plot the data points scatter around a sigmoidal curve. After fitting the parameters of a sigmoidal function to the data, the graph's characteristics represent a rather comprehensive quantitative reflex description. Variants of the 4-parameter Boltzmann sigmoidal equation are widely used for curve fitting. Unfortunately, their 'slope parameters' do not correspond to the graph's actual slope which complicates the analysis and bears the risk of misreporting. We propose a modified Boltzmann sigmoidal function with preserved goodness of fit whose parameters are one-to-one equivalent to the sigmoidal curve's characteristics.

| S-EPMC8519000 | biostudies-literature

Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty.

Project description:Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation.SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise.

| S-EPMC5518424 | biostudies-literature

Gpufit: An open-source toolkit for GPU-accelerated curve fitting.

Project description:We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets.

| S-EPMC5691161 | biostudies-literature

Sicegar: R package for sigmoidal and double-sigmoidal curve fitting.

Project description:Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmoidal data. The package categorizes data into one of three categories, "no signal," "sigmoidal," or "double-sigmoidal," by rigorously fitting a series of mathematical models to the data. The data is labeled as "ambiguous" if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as "ambiguous" rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.

| S-EPMC5774301 | biostudies-literature

Microcystin mcyA and mcyE Gene Abundances Are Not Appropriate Indicators of Microcystin Concentrations in Lakes.

Project description:Cyanobacterial harmful algal blooms (cyanoHABs) are a primary source of water quality degradation in eutrophic lakes. The occurrence of cyanoHABs is ubiquitous and expected to increase with current climate and land use change scenarios. However, it is currently unknown what environmental parameters are important for indicating the presence of cyanoHAB toxins making them difficult to predict or even monitor on time-scales relevant to protecting public health. Using qPCR, we aimed to quantify genes within the microcystin operon (mcy) to determine which cyanobacterial taxa, and what percentage of the total cyanobacterial community, were responsible for microcystin production in four eutrophic lakes. We targeted Microcystis-16S, mcyA, and Microcystis, Planktothrix, and Anabaena-specific mcyE genes. We also measured microcystins and several biological, chemical, and physical parameters--such as temperature, lake stability, nutrients, pigments and cyanobacterial community composition (CCC)--to search for possible correlations to gene copy abundance and MC production. All four lakes contained Microcystis-mcyE genes and high percentages of toxic Microcystis, suggesting Microcystis was the dominant microcystin producer. However, all genes were highly variable temporally, and in few cases, correlated with increased temperature and nutrients as the summer progressed. Interestingly, toxin gene abundances (and biomass indicators) were anti-correlated with microcystin in all lakes except the largest lake, Lake Mendota. Similarly, gene abundance and microcystins differentially correlated to CCC in all lakes. Thus, we conclude that the presence of microcystin genes are not a useful tool for eliciting an ecological role for toxins in the environment, nor are microcystin genes (e.g. DNA) a good indicator of toxins in the environment.

| S-EPMC4422731 | biostudies-literature

A Predictive Study of Dirichlet Process Mixture Models for Curve Fitting.

Project description:This paper examines the use of Dirichlet process (DP) mixtures for curve fitting. An important modelling aspect in this setting is the choice between constant or covariate-dependent weights. By examining the problem of curve fitting from a predictive perspective, we show the advantages of using covariate-dependent weights. These advantages are a result of the incorporation of covariate proximity in the latent partition. However, closer examination of the partition yields further complications, which arise from the vast number of total partitions. To overcome this, we propose to modify the probability law of the random partition to strictly enforce the notion of covariate proximity, while still maintaining certain properties of the DP. This allows the distribution of the partition to depend on the covariate in a simple manner and greatly reduces the total number of possible partitions, resulting in improved curve fitting and faster computations. Numerical illustrations are presented.

| S-EPMC4225571 | biostudies-literature

PyBindingCurve, Simulation, and Curve Fitting to Complex Binding Systems at Equilibrium.

Project description:Understanding multicomponent binding interactions in protein-ligand, protein-protein, and competition systems is essential for fundamental biology and drug discovery. Hand-deriving equations quickly become unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting, and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formations culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at https://github.com/stevenshave/pybindingcurve as the Python source code accompanied by examples and as an easily installable package within the Python Package Index.

| S-EPMC8243321 | biostudies-literature

Updating beliefs for a decision: neural correlates of uncertainty and underconfidence.

Project description:Some decisions are made after obtaining several pieces of information, whereas others are reached quickly. Such differences may depend on the quality of information acquired, as well as individual variability in how cautiously evidence is evaluated. The current study examined neural activity while subjects accumulated sequential pieces of evidence and then made a decision. Uncertainty was updated with each piece of evidence, with individual ratings of subjective uncertainty characterizing underconfidence when observing evidence. Increased uncertainty during evidence accumulation was associated with activity in dorsal anterior cingulate cortex, whereas greater uncertainty when executing a decision uniquely elicited lateral frontal and parietal activity. Greater underconfidence when observing evidence correlated with activity in ventromedial prefrontal cortex. These results suggest that neural mechanisms of uncertainty depend on the stage of decision-making (belief updating vs decision) and that greater subjective uncertainty when evaluating evidence is associated with activity in ventromedial brain regions, even in the absence of overt risk.

| S-EPMC2896864 | biostudies-literature

Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A Primer for parameter uncertainty, identifiability, and forecasts.

Project description:Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in the observed data at different spatial and temporal scales, generate estimates of key kinetic parameters, assess the impact of interventions, optimize the impact of control strategies, and generate forecasts. We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models to time series data describing the temporal progression of case counts relating to population growth or infectious disease transmission dynamics. In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters, this frequentist approach relies on modeling the error structure in the data. We discuss issues related to parameter identifiability, uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.

| S-EPMC5726591 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data