Sentence examples for 1 left hand from inspiring English sources

In the normal marginals case (Fig. 1, left hand plot), we would virtually identify no deviation of the bootstrap densities from the limiting ones.

Following solubilization with digitonin this TatBHis protein ran as a single band on BN-PAGE with an apparent molecular mass of 80 kDa (Fig. 1, left hand panel).

Figure 1 (left hand panels) shows that the AI test clearly outperforms single-SNP logistic regression in these scenarios, regardless of window size, capturing part (although not all) of the signal that is achievable through use of imputation, and often outperforming haplotype analysis (depending on the window size chosen).

Figure 1 (left hand panels) shows a comparison of the powers to achieve various P-values in three different simulation scenarios for imputation, haplotype analysis, single-SNP logistic regression in PLINK, and the AI test (with the AI test taking different SNP window sizes, see Methods) respectively.

In experiment II VEPs were recorded from 19 subjects (13 males, 1 left handed, mean age: 28.5 years).

In addition, 13 healthy controls (7 female, 1 left handed, mean age 31.6 years, range 20 58 years) were recruited, most of them from among friends and relatives of the MTLE patients.

In general the small joints of the right hand tended to show better correlation with the biometrician derived joint count compared with those of the left hand (mean kappa right hand = 0.31; left hand = 0.23; T-test P = 0.09).

In Figure 4 (left hand side), 9.8% of the area was covered by 316 [Mn III 6 Cr III ](BPh4 3 particles.

Figure  4 (left hand side) shows the simulated improvement in power for a modest treatment effect of 20% and a sample size of n = 8.

Fig. 6 Left hand column, annealed (130 °C) and freeze fractured samples: (a) PEDOT PSS reference, thickness 47 nm; (b1) PEDOT PSS on spin-coated NFC-G, thickness 138 145 nm; (c1) PEDOT PSS on drop cast NFC-G, thickness 438 458 nm.

We propose to use a combination of these three formalisms to clearly define 3D objects workflows and also geometric and semantic transformations they undergo during the entire process to meet requirements (Fig. 6 left hand side).