FT-IR results. حسب Caglar Akcay (6910697)

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Trend analysis of the ν_s – RHOB in the range of data found in the dataset used for the optimized GPR. حسب Fahd Saeed Alakbari (10701871)

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Chemical analysis of the substrates before and after cultivation (see S3 in S2 File for reference). حسب Caglar Akcay (6910697)

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FT-IR results. حسب Caglar Akcay (6910697)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.02 g/cc and DTs =  265.03 us/ft. حسب Fahd Saeed Alakbari (10701871)

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(a) Taylor diagrams of Khandelwal et al. [13], Brabdas et al, Kumar et al. [12] models. (b) Taylor diagrams of Ranjbar-Karami et al. [14], Christaras et al. [52], Feng et al. [16],... حسب Fahd Saeed Alakbari (10701871)

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FT-IR results. حسب Caglar Akcay (6910697)

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Threshold effect analysis of UACR and cognitive impairment. حسب Yubo Teng (21246745)

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Analyzing the relationship between UACR (<30) and cognitive scores and impairment. حسب Yubo Teng (21246745)

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Summary of sites of activity of genes encoded by SNPs observed to be associated in our study in the NO signaling pathway downstream of Cit. حسب Dai Nogimura (21445649)

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(a, b). The statistical error analyses for the models using the same dataset. حسب Fahd Saeed Alakbari (10701871)

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FT-IR results. حسب Caglar Akcay (6910697)

الموضوعات:

Analyzing the relationship between UACR and cognitive scores and impairment. حسب Yubo Teng (21246745)

الموضوعات:

The comparison of ν_s – RHOB trends models with experimental measurements. حسب Fahd Saeed Alakbari (10701871)

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Complete results of regression analyses of the candidate gene approach. حسب Dai Nogimura (21445649)

الموضوعات:

The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.33 g/cc and DTc = 118.67 us/ft. حسب Fahd Saeed Alakbari (10701871)

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eQTL analysis for proxy SNPs of <i>PRMT6</i>-<i>NTNG1</i> rs12028323. حسب Dai Nogimura (21445649)

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The optimized GPR approach’s cross-plotting for the training dataset. حسب Fahd Saeed Alakbari (10701871)

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Methodology flowchart. حسب Fahd Saeed Alakbari (10701871)

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The most common machine learning methods to predict ν_s. حسب Fahd Saeed Alakbari (10701871)

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