Search alternatives:
significant components » significant component (Expand Search), significant compared (Expand Search), significant improvements (Expand Search)
components analysis » component analysis (Expand Search)
linear decrease » linear increase (Expand Search)
inter decrease » water decreases (Expand Search), teer decrease (Expand Search), step decrease (Expand Search)
significant components » significant component (Expand Search), significant compared (Expand Search), significant improvements (Expand Search)
components analysis » component analysis (Expand Search)
linear decrease » linear increase (Expand Search)
inter decrease » water decreases (Expand Search), teer decrease (Expand Search), step decrease (Expand Search)
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Significantly variable serum polyunsaturated fatty acid metabolites.
Published 2024“…Linear mixed-effects models were used to identify serum metabolites that varied significantly among the storage conditions. …”
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Significantly variable serum amino acid and gamma-glutamyl amino acid metabolites.
Published 2024“…Linear mixed-effects models were used to identify serum metabolites that varied significantly among the storage conditions. …”
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Intra- and inter-day precision and accuracy.
Published 2025“…<div><p>Purpose</p><p>Statins are the most commonly used drugs worldwide. Besides a significant decrease in cardiovascular diseases (CVDs) risk, the use of statins is also connected with a broad beneficial pleiotropic effect. …”
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Principal component analysis with varimax rotation—significant loadings.
Published 2019“…<p>Principal component analysis with varimax rotation—significant loadings.…”
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Summary map of all contacts with statistically significant SVM classifications.
Published 2024“…The middle row shows the directionality of spectral change for these significant SVM clusters during number trial conditions versus inter-trial intervals. …”
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STL Linear Combination Forecast Graph.
Published 2025“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”
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