Table 1_Muscle quality index and hyperuricemia: adipose tissue as a mediator.docx by Ya Shao (16975896)

“…Each one-unit increment in MQI corresponded to a 50% decrease in the odds of hyperuricemia (OR: 0.50, 95% CI: 0.43–0.57). …”

Evaluating Quantitative Metagenomics for Environmental Monitoring of Antibiotic Resistance and Establishing Detection Limits by Benjamin C. Davis (10792515)

“…To assess the variability of low-abundance oligonucleotide detection across sample matrices, we spiked DNA reference standards (meta sequins) into replicate wastewater DNA extracts at logarithmically decreasing mass-to-mass percentages (m/m%). Meta sequin ladders exhibited strong linearity at input concentrations as low as 2 × 10^–3 m/m% (<i>R</i>² > 0.95), with little to no reference length or GC bias. …”

S2 Data - by David E. B. Fleming (14835322)

“…Coefficients of determination (r²) were stronger for zinc (from 0.74 to 0.95) than selenium (from 0.53 to 0.70). A decrease in XRF measurement time from 300 s to 180 s did not appear to adversely affect the correlation between XRF and ICP-MS results. …”

S1 Data - by David E. B. Fleming (14835322)

“…Coefficients of determination (r²) were stronger for zinc (from 0.74 to 0.95) than selenium (from 0.53 to 0.70). A decrease in XRF measurement time from 300 s to 180 s did not appear to adversely affect the correlation between XRF and ICP-MS results. …”

Prediction of individual spatial patterns of nodal module overlap from structural brain features in children. by Tianyuan Lei (19683861)

Analysis of PRV infection in mouse spleen samples. by Lucai Wang (22050158)

Relationship between the antioxidant effect of the extracts and their cannabinoid content. by Bruno Musetti (9527085)

Application of model using two principal components. by Monica E. Shapiro (22622240)

Optimized TMS recapitulates DNA methylation levels measured with the EPIC array and WGBS. by Amy Longtin (21409957)

Fig 6 - by Xiajun Zhang (3161865)

Image 2_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 8_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 7_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 3_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 10_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 4_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 6_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 5_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 1_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 9_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)