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lowess regression  (ChangePoint Inc)

 
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    ChangePoint Inc lowess regression
    Red points in ( A ) to ( C ) denote samples with Fe T > 0.5%; blue points, Fe T 0.4 to 0.5%; black points, Fe T < 0.4%. Blue band in (B) marks sediments deposited under oxic conditions, and green band marks sediments deposited under anoxic conditions. In (C), green band represents ferruginous conditions, and orange band represents euxinic conditions. In ( D ), green band marks <t>near-zero</t> <t>δ</t> 15 N bulk values indicative of dominance of nitrogen fixation. Color and size of data points are keyed to reflect TN and TOC contents. Blue line is <t>LOWESS</t> regression fit with 95% CI (gray shadow) calculated from SEs.
    Lowess Regression, supplied by ChangePoint Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/linear+locally+weighted+scatterplot+smoother+(lowess)+method/lowess+regression/pmc10032604-215-26-29
    Average 90 stars, based on 1 article reviews
    lowess regression - by Bioz Stars, 2026-11
    90/100 stars

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    1) Product Images from "Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes"

    Article Title: Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes

    Journal: Science Advances

    doi: 10.1126/sciadv.ade9647

    Red points in ( A ) to ( C ) denote samples with Fe T > 0.5%; blue points, Fe T 0.4 to 0.5%; black points, Fe T < 0.4%. Blue band in (B) marks sediments deposited under oxic conditions, and green band marks sediments deposited under anoxic conditions. In (C), green band represents ferruginous conditions, and orange band represents euxinic conditions. In ( D ), green band marks near-zero δ 15 N bulk values indicative of dominance of nitrogen fixation. Color and size of data points are keyed to reflect TN and TOC contents. Blue line is LOWESS regression fit with 95% CI (gray shadow) calculated from SEs.
    Figure Legend Snippet: Red points in ( A ) to ( C ) denote samples with Fe T > 0.5%; blue points, Fe T 0.4 to 0.5%; black points, Fe T < 0.4%. Blue band in (B) marks sediments deposited under oxic conditions, and green band marks sediments deposited under anoxic conditions. In (C), green band represents ferruginous conditions, and orange band represents euxinic conditions. In ( D ), green band marks near-zero δ 15 N bulk values indicative of dominance of nitrogen fixation. Color and size of data points are keyed to reflect TN and TOC contents. Blue line is LOWESS regression fit with 95% CI (gray shadow) calculated from SEs.

    Techniques Used:

    ( A ) Compilation of published nitrogen isotope data from Mesoproterozoic to Neoproterozoic. Boxplots show distribution of δ 15 N values in each 100-Ma time bin. Blue line is LOWESS regression fit of lowest 25% δ 15 N data in every 100-Ma time bin; see fig. S6 for LOWESS regression fit of lowest 50% data and the entire dataset. Gray shade is 95% CI calculated from 10,000 bootstrapping experiments. Red triangle marks stepwise increase from a changepoint analysis of lowest 25% δ 15 N data. Most significant change in both mean and variance occurs at ca. 820 Ma. The green shade marks δ 15 N values characteristic of a nitrogen cycle dominated by nitrogen fixation (e.g., f assimilator < 0.16; see <xref ref-type=Fig. 5 ). ( B ) Frequency distributions of δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples. ( C ) Frequency distributions of mean δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples. " title="... in each 100-Ma time bin. Blue line is LOWESS regression fit of lowest 25% δ 15 N ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend Snippet: ( A ) Compilation of published nitrogen isotope data from Mesoproterozoic to Neoproterozoic. Boxplots show distribution of δ 15 N values in each 100-Ma time bin. Blue line is LOWESS regression fit of lowest 25% δ 15 N data in every 100-Ma time bin; see fig. S6 for LOWESS regression fit of lowest 50% data and the entire dataset. Gray shade is 95% CI calculated from 10,000 bootstrapping experiments. Red triangle marks stepwise increase from a changepoint analysis of lowest 25% δ 15 N data. Most significant change in both mean and variance occurs at ca. 820 Ma. The green shade marks δ 15 N values characteristic of a nitrogen cycle dominated by nitrogen fixation (e.g., f assimilator < 0.16; see Fig. 5 ). ( B ) Frequency distributions of δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples. ( C ) Frequency distributions of mean δ 15 N values from bootstrapping experiments ( n = 10,000) of pre–800 Ma (blue) and post–800 Ma (orange) samples.

    Techniques Used:

    ( A ) LOWESS regression curve of lowest 25% δ 15 N data in every 100-Ma time bin. A stepwise rise in δ 15 N at ca. 800 Ma coincides with breakup of supercontinent Rodinia and implies increasing marine nitrate availability. ( B ) Phosphorus concentrations of marine siliciclastic sediments through time ( , ). LOWESS regression is based on the entire dataset. An increase in late Tonian implies greater phosphorus availability in the surface ocean. ( C ) Biomarker data (see the Supplementary Materials) . Note oldest steranes at ca. 820 Ma, increasing sterane/hopane ratios, and inferred dominance of various primary producers. ( D ) Approximate stratigraphic ranges of major eukaryotic clades .
    Figure Legend Snippet: ( A ) LOWESS regression curve of lowest 25% δ 15 N data in every 100-Ma time bin. A stepwise rise in δ 15 N at ca. 800 Ma coincides with breakup of supercontinent Rodinia and implies increasing marine nitrate availability. ( B ) Phosphorus concentrations of marine siliciclastic sediments through time ( , ). LOWESS regression is based on the entire dataset. An increase in late Tonian implies greater phosphorus availability in the surface ocean. ( C ) Biomarker data (see the Supplementary Materials) . Note oldest steranes at ca. 820 Ma, increasing sterane/hopane ratios, and inferred dominance of various primary producers. ( D ) Approximate stratigraphic ranges of major eukaryotic clades .

    Techniques Used: Biomarker Discovery

    Related Articles

    Biomarker Discovery:

    Article Title: Nitrate limitation in early Neoproterozoic oceans delayed the ecological rise of eukaryotes
    Article Snippet: We note that more data from the 800- to 900-Ma time bin are needed to accurately define the rise of δ 15 N. Sensitivity tests of LOWESS regression and changepoint detection analysis on the lowest 50% data and the entire dataset (fig. S6) also revealed a stepwise rise at around 800 Ma, although these analyses show more temporal variation.



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    Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed <t>(LOWESS)</t> reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.
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    Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

    Journal: Biotechnology and Bioengineering

    Article Title: Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

    doi: 10.1002/bit.26214

    Figure Lengend Snippet: Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

    Article Snippet: As the derivative can amplify possible biological and analytical errors, the specific production rates were, preliminarily to deriving, smoothed as a function of the specific growth rate with the linear Locally Weighted Scatterplot Smoother (LOWESS) method (Cleveland, ) by using SAS software JMP 11 ©.

    Techniques: Cell Culture, Plasmid Preparation, Biomarker Discovery