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Patients and methods We decided to include ARDS patients under mechanical ventilation sedated and paralyzed with P/F < 200.

After preliminary experiments with several machine learning methods we decided to adopt Random Forests [44] as our predictive modelling tool of choice.

After validation of these antibodies by using kinase assays, western blot and ELISA methods, we decided to use these the antibodies to determine the impact of the Akt/PKC-induced phosphorylations of Dnmt1 on the Dnmt1/PCNA and Dnmt1/UHRF1 interactions (Supplemental data S7 and Supplemental data S8).

As part of our ongoing interest in developing stereoselective glycosylation methods, we decided to focus our attention on the synthesis of deoxyglycosides.

Since binding of Sp1 is well studied with experimental methods, we decided to assess Sp1 binding in CGIs of different localization relative to known genes.

For easier comparison of the two methods, we decided to let positive values indicate PA, in contrast to Chen et al. who used negative values to indicate protrusion.

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As global stochastic optimization methods we decide to use evolutionary approaches such as Stochastic Ranking Evolutionary Search SRESS) [ 27] or Differential Evolution (DE) [ 28].

method, we decided to set the impurity limits for hydrophilic and lipophilic impurities to 3%.

In addition to this method, we decided to include a separate strategy in which log phase cells were synchronized in early S-phase using medium supplemented with hydroxyurea (HU), which produced a more effective synchronization (data not shown).

After validation of this method, we decided to generate shRNAs from partially randomized oligonucleotides following the procedure in Fig. 1.

Since fold change values strongly depend upon the selected normalization method, we decided to introduce the option to filter the modulated genes on fold change percentile ranking.

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