Fig 5 - by Dennis Ochola (11626912)

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

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)

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

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

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

Fig 9 - by Dennis Ochola (11626912)

Fig 7 - by Dennis Ochola (11626912)

Fig 4 - by Dennis Ochola (11626912)

HRV parameters are correlated with mood states and plasma epinephrine level. by Shiying Liu (1569478)

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). …”

Table1_Effects of SPAD value variations according to nitrogen application levels on rice yield and its components.xlsx 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). …”

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

Family History of Hypertension Impairs the Autonomic Balance, but not the Endothelial Function, in Young Soccer Players by Walter Vargas (6798635)

“…The significance level adopted in the statistical analysis was 5%. Results The standard deviation of normal RR intervals (SDNN; FM-N=314±185; FM-H=182.4± 57.8), the square root of the mean squared differences in successive RR intervals (RMSSD; FM-N=248±134; FM-H=87±51), the number of interval differences of successive NN intervals greater than 50ms (NN50; FM-N=367±83.4; FM-H=229±55), the ratio derived by dividing NN50 by the total number of NN intervals (pNN50; FM-N=32.4±6.2; FM-H=21.1±5.3) and the high (HF; FM-N=49±8.9; FM-H=35.3±12) and low-frequency (LF; FM-N=50.9±8.9; FM-H=64.6±12) components, in normalized units (%), were significantly lower in the FM-H group than in the FM-N group (p<0.05). …”

EFFECTS OF ADAPTED CANOEING ON THE CARDIOPULMONARY SYSTEM OF PARAPLEGICS by Elizângela Márcia de Carvalho Abreu (6147902)

“…The HF indices (un) and LF/HF showed no significant decrease of 5.7 and 7.0%, respectively. The highest breathing gains were for MVV (9.7%), MIP (8.5%) and MEP (11.0%), being however not significant. …”

Improvement in Blood Pressure After Intermittent Fasting in Hipertension: Could Renin-Angiotensin System and Autonomic Nervous System Have a Role? by Erkan Demirci (15341193)

“…An increase in high-frequency (HF) power and the mean root square of the sum of squares of differences between adjacent NN intervals (RMSSD) were observed after the IF protocol (p=0.039, p=0.043). …”

Supplementary Material for: Risk factors and heart rate variability associated with left ventricular enlargement in patients with frequent premature ventricular contractions by Yang Z. (4129039)

“…The values of SDNN, standard deviation of the averages of NN intervals in all 5-min segments, the square root of the mean of the sum of the squares of differences between adjacent NN intervals, pNN50, low-frequency power (LF), LF/high-frequency power ratio of patients were significantly decreased after ablation (all p<0.05). …”

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)

“…We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline).</p><p>Design: This retrospective cohort study used three models based on LR, k-nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. …”