Quantifying errors in trace species transport modeling.
Ontology highlight
ABSTRACT:
SUBMITTER: Prather MJ
PROVIDER: S-EPMC2598731 | biostudies-literature | 2008 Dec
REPOSITORIES: biostudies-literature
Publications
Quantifying errors in trace species transport modeling.
Prather Michael J MJ Zhu Xin X Strahan Susan E SE Steenrod Stephen D SD Rodriguez Jose M JM
Proceedings of the National Academy of Sciences of the United States of America 20081209 50
One expectation when computationally solving an Earth system model is that a correct answer exists, that with adequate physical approximations and numerical methods our solutions will converge to that single answer. With such hubris, we performed a controlled numerical test of the atmospheric transport of CO(2) using 2 models known for accurate transport of trace species. Resulting differences were unexpectedly large, indicating that in some cases, scientific conclusions may err because of lack ...[more]
Similar Datasets
Project description:BackgroundAll quantifications of mortality, morbidity, and other health measures involve numerous sources of error. The routine quantification of random sampling error makes it easy to forget that other sources of error can and should be quantified. When a quantification does not involve sampling, error is almost never quantified and results are often reported in ways that dramatically overstate their precision.DiscussionWe argue that the precision implicit in typical reporting is problematic and sketch methods for quantifying the various sources of error, building up from simple examples that can be solved analytically to more complex cases. There are straightforward ways to partially quantify the uncertainty surrounding a parameter that is not characterized by random sampling, such as limiting reported significant figures. We present simple methods for doing such quantifications, and for incorporating them into calculations. More complicated methods become necessary when multiple sources of uncertainty must be combined. We demonstrate that Monte Carlo simulation, using available software, can estimate the uncertainty resulting from complicated calculations with many sources of uncertainty. We apply the method to the current estimate of the annual incidence of foodborne illness in the United States.SummaryQuantifying uncertainty from systematic errors is practical. Reporting this uncertainty would more honestly represent study results, help show the probability that estimated values fall within some critical range, and facilitate better targeting of further research.
| S-EPMC166164 | biostudies-literature
Project description:Escherichia coli strains are widely used in academic and applied research as well as in biotechnology for production of various compounds. Despite its status as a model organism, strain-specific differences and their underlying contributing factors are still not well characterized. These differences have a major impact on cell physiology and for the applied purposes of synthetic biology, metabolic engineering, and process design. In this study, strain-specific differences are quantified in seven widely-used applied biotechnology strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics and genome-scale modelling to guide the choice of strain for a given product. Even given the genetic similarity of the strains, mMetabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titre. Further, these differences can be linked to differential regulatory structure. Analysing high flux reactions and the expression levels of their encoding genes revealed a quantitative link between these sets and show that often, these sets are correlated with strain-specific caveats. Integrated modelling also revealed that certain strains are better suited to produce a given compound or express a desired construct considering native expression states of pathways that enable high-production phenotypes. The result of this study is a resource comparing strains in an important model species and a general strategy for choosing a host strain or chassis selection for applied biotechnology.
2016-09-22 | GSE78756 | GEO
Project description:This paper presents process models for a new micro additive manufacturing process termed Electrophoretically-guided Micro Additive Manufacturing (EPμAM). In EPμAM, a planar microelectrode array generates the electric potential distributions which cause colloidal particles to agglomerate and deposit in desired regions. The discrete microelectrode array nature and the used pulse width modulation (PWM) technique for microelectrode actuation create unavoidable process errors-space and time discretization errors-that distort particle trajectories. To combat this, we developed finite element method (FEM) models to study trajectory deviations due to these errors. Mean square displacement (MSD) analysis of the computed particle trajectories is used to compare these deviations for several electrode geometries. The two top-performing electrode geometries evaluated by MSD were additionally investigated through separate case studies via geometry variation and MSD recomputation. Furthermore, separate time-discretization error simulations are also studied where electrode actuating waveforms were simulated. The mechanical impulse of the electromechanical force, generated from these waveforms is used as the basis for comparison. The obtained results show a moderate MSDs variability and significant differences in the computed mechanical impulses for the actuating waveforms. The observed limitations of the developed process model and of the error comparison technique are briefly discussed and future steps are recommended.
| S-EPMC6187608 | biostudies-literature
Project description:Secondary active transporters, which are vital for a multitude of physiological processes, use the energy of electrochemical ion gradients to power substrate transport across cell membranes1,2. Efforts to investigate their mechanisms of action have been hampered by their slow transport rates and the inherent limitations of ensemble methods. Here we quantify the activity of individual MhsT transporters, which are representative of the neurotransmitter:sodium symporter family of secondary transporters3, by imaging the transport of individual substrate molecules across lipid bilayers at both single- and multi-turnover resolution. We show that MhsT is active only when physiologically oriented and that the rate-limiting step of the transport cycle varies with the nature of the transported substrate. These findings are consistent with an extracellular allosteric substrate-binding site that modulates the rate-limiting aspects of the transport mechanism4,5, including the rate at which the transporter returns to an outward-facing state after the transported substrate is released.
| S-EPMC7211416 | biostudies-literature
Project description:Axonal transport of mitochondria is critical for neuronal survival and function. Automatically quantifying and analyzing mitochondrial movement in a large quantity remain challenging. Here, we report an efficient method for imaging and quantifying axonal mitochondrial transport using microfluidic-chamber-cultured neurons together with a newly developed analysis package named "MitoQuant". This tool-kit consists of an automated program for tracking mitochondrial movement inside live neuronal axons and a transient-velocity analysis program for analyzing dynamic movement patterns of mitochondria. Using this method, we examined axonal mitochondrial movement both in cultured mammalian neurons and in motor neuron axons of Drosophila in vivo. In 3 different paradigms (temperature changes, drug treatment and genetic manipulation) that affect mitochondria, we have shown that this new method is highly efficient and sensitive for detecting changes in mitochondrial movement. The method significantly enhanced our ability to quantitatively analyze axonal mitochondrial movement and allowed us to detect dynamic changes in axonal mitochondrial transport that were not detected by traditional kymographic analyses.
| S-EPMC5084152 | biostudies-literature
Project description:Quantifying fluxes of trace elements and their isotopes (TEIs) at the ocean's sediment-water boundary is a pre-eminent challenge to understand their role in the present, past and future ocean. There are multiple processes that drive the uptake and release of TEIs, and properties that determine their rates are unevenly distributed (e.g. sediment composition, redox conditions and (bio)physical dynamics). These factors complicate our efforts to find, measure and extrapolate TEI fluxes across ocean basins. GEOTRACES observations are unveiling the oceanic distributions of many TEIs for the first time. These data evidence the influence of the sediment-water boundary on many TEI cycles, and underline the fact that our knowledge of the source-sink fluxes that sustain oceanic distributions is largely missing. Present flux measurements provide low spatial coverage and only part of the empirical basis needed to predict TEI flux variations. Many of the advances and present challenges facing TEI flux measurements are linked to process studies that collect sediment cores, pore waters, sinking material or seawater in close contact with sediments. However, such sampling has not routinely been viable on GEOTRACES expeditions. In this article, we recommend approaches to address these issues: firstly, with an interrogation of emergent data using isotopic mass-balance and inverse modelling techniques; and secondly, by innovating pursuits of direct TEI flux measurements. We exemplify the value of GEOTRACES data with a new inverse model estimate of benthic Al flux in the North Atlantic Ocean. Furthermore, we review viable flux measurement techniques tailored to the sediment-water boundary. We propose that such activities are aimed at regions that intersect the GEOTRACES Science Plan on the basis of seven criteria that may influence TEI fluxes: sediment provenance, composition, organic carbon supply, redox conditions, sedimentation rate, bathymetry and the benthic nepheloid inventory.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.
| S-EPMC5069539 | biostudies-literature
Project description:Single-nucleotide-resolution chemical mapping for structured RNA is being rapidly advanced by new chemistries, faster readouts, and coupling to computational algorithms. Recent tests have shown that selective 2'-hydroxyl acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in modeling the helices of RNA secondary structure. Here, we benchmark the method using six molecules for which crystallographic data are available: tRNA(phe) and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs gave an overall false negative rate (FNR) of 17% and a false discovery rate (FDR) of 21%, with at least one helix prediction error in five of the six cases. Extensive variations of data processing, normalization, and modeling parameters did not significantly mitigate modeling errors. Only one varation, filtering out data collected with deoxyinosine triphosphate during primer extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual structure modeling errors are explained by the insufficient information content of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping analysis. Beyond these benchmark cases, bootstrapping suggests a low level of confidence (<50%) in the majority of helices in a previously proposed SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA modeling is not always unambiguous, and helix-by-helix confidence estimates, as described herein, may be critical for interpreting results from this powerful methodology.
| S-EPMC3172344 | biostudies-literature
Project description:BACKGROUND: High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of kmers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous kmer may be frequently observed if it has few nucleotide differences with valid kmers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content. RESULTS: We develop a statistical model and a computational method for error detection and correction in the presence of genomic repeats. We propose a method to infer genomic frequencies of kmers from their observed frequencies by analyzing the misread relationships among observed kmers. We also propose a method to estimate the threshold useful for validating kmers whose estimated genomic frequency exceeds the threshold. We demonstrate that superior error detection is achieved using these methods. Furthermore, we break away from the common assumption of uniformly distributed errors within a read, and provide a framework to model position-dependent error occurrence frequencies common to many short read platforms. Lastly, we achieve better error correction in genomes with high repeat content. AVAILABILITY: The software is implemented in C++ and is freely available under GNU GPL3 license and Boost Software V1.0 license at "http://aluru-sun.ece.iastate.edu/doku.php?id = redeem". CONCLUSIONS: We introduce a statistical framework to model sequencing errors in next-generation reads, which led to promising results in detecting and correcting errors for genomes with high repeat content.
| S-EPMC3044310 | biostudies-literature
Project description:Nanoparticles are applied as versatile platforms for drug/gene delivery in many applications owing to their long-retention and specific targeting properties in living bodies. However, the delivery mechanism and the beneficial effect of nanoparticle-retention in many organisms remain largely uncertain. Here, the transport and metabolism of mineral nanoparticles in mammary gland during lactation are explored. It is shown that maternal intravenous administration of iron oxide nanoparticles (IONPs; diameter: ≈11.0 nm, surface charge: -29.1 mV, surface area: 1.05 m2 g-1 ) provides elevated iron delivery to mammary gland and increased iron secretion into breast milk, which is inaccessible by classical iron-ion transport approaches such as the transferrin receptor-mediated endocytic pathway. Mammary macrophages and neutrophils are found to play dominant roles in uptake and delivery of IONPs through an unconventional leukocyte-assisted iron secretion pathway. This pathway bypasses the tight iron concentration regulation of liver hepcidin-ferroportin axis and mammary epithelial cells to increase milk iron-ion content derived from IONPs. This work provides keen insight into the metabolic pathway of nanoparticles in mammary gland while offering a new scheme of nutrient delivery for neonate metabolism regulation by using nanosized nutrients.
| S-EPMC9475556 | biostudies-literature
Project description:Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.
| S-EPMC3198479 | biostudies-literature