The G215S mutation decreases packaging of N and growth in primary differentiated human bronchial cells. by Hannah C. Kubinski (21213168)

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

Positional cloning of <i>qNN1</i> on chromosome 16. 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)

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

Fli-1 directly activates miR-17-92 promoter and contributes to miR-17 and miR-20a expression in NN10#5 cells. by Samer Kayali (133888)

“…<p>A: NN10#5 cells have been cultured for two days in absence or presence of Dox used to induce Fli-1 knock-down before analysis. …”

HRV parameters at different time points. by Shiying Liu (1569478)

Fig 4 - by Dennis Ochola (11626912)

Fig 5 - 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. …”

Relative expression levels of symbiotic marker genes in the roots of different plant materials. by Xinxin Li (281237)

Barcoding Beetles: A Regional Survey of 1872 Species Reveals High Identification Success and Unusually Deep Interspecific Divergences by Mikko Pentinsaari (634506)

Pol II, nucleosome, and H3K4me3 dynamics at circadian promoters. by Pål O. Westermark (3140802)

Nuclear HER3 can regulate a minimal region of the cyclin D1 promoter via its bipartite transactivation domain. by Toni M. Brand (443217)

Networks with passive cross-links and no filament turnover undergo three stages of deformation in response to an extensional stress. by William M. McFadden (4692130)

“…Note that the stress is nearly constant and the velocity is nearly linear as predicted for a viscous fluid under extension. <b>C)</b> Plots of the mean stress and cumulative network strain vs time for the simulation in (B), illustrating the three stages of deformation: (i) A fast initial deformation accompanies rapid buildup of internal network stress; (ii) after a characteristic time <i>τ</i>_<i>c</i> (indicated by vertical dotted line) the network deforms at a constant rate, i.e. with a constant effective viscosity, <i>η</i>_<i>c</i>, given by the slope of the dashed line; (iii) at long times, decrease in filament density, and loss of network connectivity, leads to material failure (see inset).…”

Supplementary Material for: Demographic Risk Factors of Retinopathy of Prematurity: A Systematic Review of Population-Based Studies by Yu C.W. (10945386)

Table_1_Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait.DOCX by Bassam Farran (553880)

Inferring intrinsic noise intensities and reconstructing experimental data via END-nSDE. by Jiancheng Zhang (181059)

Image1_Effects of SPAD value variations according to nitrogen application levels on rice yield and its components.pdf by Tae-Heon Kim (2013925)

“…The SPAD value ranged between 30.5 and 55.8, with a mean of 41.7 ± 5.3, under normal nitrogen conditions (NN, 9 kg/10a), and between 27.5 and 52.3, with a mean of 38.6 ± 4.8, under low nitrogen conditions (LN, 4.5 kg/10a). …”