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The mixtures were activated with alkaline solutions and they were cured by using two different conditions: at room temperature (25 °C) and in a thermal bath (65 °C).
The study was conducted using two different conditions: 1) chemical sensor stored in a volumetric flask wrapped in aluminium foil and placed in the refrigerator; and 2) chemical sensor stored in a volumetric flask without being wrapped in aluminium foil and placed at room temperature.
We performed OGD experiment using two different conditions: DBSS without glucose and (b)) or DMEM without glucose and (b)).
300 μg pooled protein was separated by 2D-PAGE using two different conditions for IEF and SDS-PAGE.
For HRTEM measurements, we used only the AMS targets of GCIP that were prepared using two different conditions (C/Fe = 1 5, 500 °C, 3 h(11) versus C/Fe = 1 1, 585 °C, 3 h).
To further increase the likelihood of full coverage, the strobe-sequencing run was performed using two different conditions: I) 45 min light period (continuous collection time); and II) (5 min light period, 10 min dark period), followed by (45 min light period, 10 min dark period).
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Rhodopsin cDNA of the Ayu fish (Plecoglossus altivelis) was used as a template and was amplified using five different conditions: (A) 100% H2O with no Mn2+, (B) 100% H2O/0.6 mM Mn2+, (C) 99% D2O with no Mn2+, (D) 99% D2O/0.6 mM Mn2+ and (E) 99% H218O with no Mn2+.
Glioma-bearing mice were treated using four different conditions: group I: vehicle only (GM-CSF and Montanide); group II: three immunizations spaced one week apart (days 9, 16 and 23) with two 9-mer and two 10-mer peptides; group III: three immunization spaced one week apart (days 9, 16 and 23) with the 16-mer peptide; group IV: ova peptide immunizations.
The sample was divided into portions and fragmented using five different conditions: 1 min AluI digestion; 3 h AluI digestion, 6 min nebulisation at 35 psi; 6 min nebulisation at 35 psi followed by 1 min AluI digestion, and 6 min nebulisation followed by 3 h AluI digestion.
These include changing the probability density function from Gaussian to super-Gaussian [3] or by de-correlation of the output streams [13, 26].The hybrid algorithm proposed here uses two different conditions instead of one.
After they had successfully learned the three-term sequence, we used two different conditions to test what and how they learned in the baseline training.
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