Sentence examples for untreated datasets from inspiring English sources

We computed K a / K s values from two untreated datasets: 2,628 HIV-positive samples from U.S. patients who had received no drug treatment (Untreated); and 399 HIV-positive samples from African patients (predominantly untreated [ 18- 20]).

The Treated and the Untreated datasets were downloaded from Stanford database (http://hivdb.stanford.edu/) [53], selecting only subtype B sequences.

Previous studies have showed that comparison of these Treated vs. Untreated datasets can identify the effects of antiviral drug treatment [15], [18].

Fourth, we also found direct evidence that the difference in covariation levels between (A,A) vs. (A,S /(S,S) is due to selection, specifically, antiviral drug treatment, by comparing treated vs. untreated datasets.

These were grouped in three clusters based on the comparison of mutation rates between treated and untreated datasets.

To test its robustness to such contamination, we deliberately mixed the Treated and Untreated datasets, and analyzed K a / K s from the mixed data.

Such an artifact should have also have been observed in the untreated dataset.

Strikingly, the large difference in covariation between (A,A) and (A,S /(S,S) disappeared in the untreated dataset.

Yet, the difference between (A,A) vs. (A,S /(S,S) disappeared in the untreated dataset (Fig. 3), indicating that this difference was due specifically to drug-treatment.

This suggests that drug treatment (shared by the Specialty and the Treated datasets, but not the Untreated dataset) causes the nearly identical pattern of selective interactions found in these two independent datasets.

Finally, all aspects of the conditioned changes in GLR-1 clustering were rescued when magi-1 was simultaneously expressed in RIA, AVA and AVD using the glr-1 and glr-6 promoters (Fig. 5d) (1.3±0.0682 µm, n = 170, p = 5.83*10−5, two-tailed t-test compared to untreated dataset).