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Ab initio predictions gave 30, 43 and 55 predicted gene structures with the FGENESH gene finder trained respectively for grapevine, tomato and tobacco gene models.

The research team included one educated older man who spoke the local language, one female leader who spoke the local language, one female academically trained researcher who spoke the local language, one academically trained researcher who spoke Bahasa Indonesia and English, and two Australian academics trained respectively in medical anthropology and geography.

The instances are saved in feature matrix X, labeled with "yes" and "no", indicating positive and negative ones, respectively; second, the J48 Decision Tree (J48) classifier and Random Forests (RF) classifier are trained respectively, with 10-fold cross-validation to avoid over-fitting; finally, new RNA-Seq data are sent to the two classifiers respectively for IR event prediction.

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They had received 18 and three months training respectively and knew the communities in question.

The EPS-triggered changes of these genes in muscle fibers in culture were then compared to those in acutely exercised and trained mice, respectively.

Pre-training and post-training, before (T1) and after (T2) 12-week of high-speed power training, respectively.

Born in Philadelphia, she studied at Harvard, Stanford and the University of Pennsylvania for biochemistry, medicine and anesthesia training, respectively.

Results indicate that trains and trucks experience transient phenomena when traveling over train switches, accelerating and stopping the train, respectively road unevenness (e.g. potholes).

Both the memory trainers and the controls underwent magnetic resonance imaging (MRI) scans and memory testing pre and post 8 weeks of training or no training, respectively.

Box Behnken design (BBD) and Central Composite design (CCD) are applied to collect a small dataset for neural network training, respectively.

Overall, 75% of the sample lived within the recommended catchment of a bus stop, compared with 19.8% and 18.0% for trams and trains, respectively.

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