Sentence examples for gm using a from inspiring English sources

The second, however, only computes a rough approximation of the actual GM using a proposed heuristic method based on Arithmetic-Geometric-Harmonic (AGH) mean.

Examination included measurement of height in stocking feet (to the nearest 0.1 cm) using a fixed arm stadiometer and weight in underclothes (to the nearest 100 gm) using a balance scale.

Data were independently extracted by two reviewers (FP and GM) using a predesigned data collection form: (1) number of subjects; geographic origin of the study; relaxation training type; (4) subjects typology; (5) mean age and women percentage; (6) assessment measures; (7) homework; (8) protocol length; (9) trial context; (10) summary statistics required for computation of effect sizes.

Weight was measured and recorded to the nearest 100 gm using an ordinary bathroom scale, and waist and hip circumference were measured using a non-elastic tape meter.

Using both the Pv/Gm syntentic data as well the duplications found in Gm using the large data set from Pv, several conclusions can be drawn regarding the chromosomal history of soybean.

It is thus interesting to investigate the performance of GM-SMCC using a single latent graph, and the performance of EGM-SMCC utilizing multiple latent graphs.

In parallel to investigation of the local inflammation induced by TPA RNAA expression analysis of skin tissues), we assessed the systemic cytokine response in TPA-treated mice 6 h after TPA treatment by analyzing serum levels of 10 different cytokines: IL-1α, IL-2, IL-4, IL-5, IL-6, IFN-γ, IL-10, IL-17, TNF-α, and GM-CSF (using a cytometric bead array).

Neutralizing GM-CSF using an anti-GM-CSF monoclonal antibody significantly inhibited IFN- γ, TNF- α, and IL-12p70 elicited by SCG.

A digitally programmable low-voltage highly linear transconductor (Gm stage) realization, using a promising CMOS structure of differential difference current conveyor (DDCC) and a R-2R ladder network, is introduced in this paper.

From Table 1, we observe that EGM-SMCC using multiple latent graphs is able to achieve better performance against the GM-SMCC method using a single latent graph.

According to the figures, we can see that EGM-SMCC consistently outperforms the GM-SMCC algorithms using a single latent graph because more information are utilized.