Top k-NN clusters of metabolites decreased at 4 hours. by Zarazuela Zolkipli-Cunningham (5234186)

Top k-NN cluster of metabolites decreased at 30 minutes. by Zarazuela Zolkipli-Cunningham (5234186)

NN analysis to assign the importance of variables. by Sulmaz Ghahramani (3387635)

On BirdVox-full-night, the recall of the baseline CNN increases with time, as the density of flight calls increases and the noise level decreases. by Vincent Lostanlen (5803529)

Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

Positional cloning of <i>qNN1</i> on chromosome 16. by Xinxin Li (281237)

<i>GmNN1/FT2a</i> expression analysis. by Xinxin Li (281237)

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

Subjects:

Shoot-to-root translocation of GmNN1/FT2a regulates soybean nodulation. by Xinxin Li (281237)

Functions of <i>GmNN1/FT2a</i> in nitrogen nutrition and nodulation. by Xinxin Li (281237)

Plant growth, nodulation, and N content as affected by manipulating of GmNN1/FT2a. by Xinxin Li (281237)

Expression of <i>GmNN1/FT2a</i> in various tissues of soybean plants. by Xinxin Li (281237)

GmNN1/FT2a interacted with GmNYFA-C in the BiFC assay. by Xinxin Li (281237)

Temporal profiles of the key BO-NN features. by Julia Berezutskaya (9080269)

“…A linear model was trained per each audio-ECoG shift in the range of -600 ms to 1 s. Spearman correlations between the predicted and observed activity of the key BO-NN features were calculated and averaged over the six cross-validation folds. …”

Gene specific primers used for <i>GmNN1/FT2a</i> fine mapping. by Xinxin Li (281237)

Phenotype and molecular identification of <i>GmNN1/FT2a</i> in stable transgenic soybean plants. by Xinxin Li (281237)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. by Dennis Ochola (11626912)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. by Dennis Ochola (11626912)

SLE decreases risk for hormonal cancers. by Deborah K. Johnson (10001156)

“…<p>Odds ratios (OR) and 95% confidence intervals of SLE and MS cohorts. For all graphs, the dotted line represents an OR of 1.0. …”

The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation. by Xinxin Li (281237)