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

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

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)

“…Right plot show the gradual decrease in the TM tuning for the features highlighted in <b>c</b> as well as the gradual increase in the temporal response profile (i.e. optimal shifts for the prediction of the key BO-NN features using Praat features shown in <b>a</b>). …”

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

Knocking down of TRIM59 decreased the expression of cell cycle proteins. by Weihua Zhan (830500)

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

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

Ifit2 deficiency results in decreased neuroinflammation upon RSA59 infection. by Jayasri Das Sarma (652473)

A vector quantization based k-NN approach for large-scale image classification by Ozan, Ezgi Can

“…In this paper we propose a vector quantization (VQ) based k-NN classifier, which has improved efficiency for both storage requirements and computational costs. …”
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Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

Subjects:

A proposed working model of how the GmNN1/FT2a-GmNYFA-C-GmENOD40 module regulates nodulation in soybean. 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)

The effect of novel synthetic communities on plant shoot Pi content can be predicted by an NN. by Sur Herrera Paredes (674875)

“…<p>(<b>A</b>) Schematic representation of the NN defined and applied for predictions. …”

Design and analysis of PV fed high-voltage gain DC-DC converter using PI and NN controllers by Madisa V.G., Varaprasad

“…When the converter switch comes under active state two capacitors are discharged in series, similarly when the converter switch under inactive state those two capacitors are charged in parallel through the coupled inductor (CI) energy so, to get a high voltage at the converter output side. To reuse the leakage-inductor (LI) energy of the CI with the help of a passive clamp circuit for reducing the voltage stress across the switch, similarly switch has less resistance so conduction losses also decreased. …”
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