Effect of Andro and/or Taxi on the proliferation of HeLa cells. by Mazen Alzaharna (3737878)

“…Values from each time point were then compared to the control values and expressed as mean ± SD of three independent experiments. (D) A graph representing the decrease in the total concentration of Andro and Taxi when used in combination compared to the single doses of Andro or Taxi.…”

Constitutive promoter activity in long term culture of undifferentiated hESCs. by Karin Norrman (242990)

“…<b>A</b>–<b>C</b>. Data are shown as mean of three independent experiments. …”

The effect of 4 day’s starvation on visible protein aggregates. by Ulfat I. Baig (233369)

“…<p>(n = 100±20 each for data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107445#pone-0107445-g002" target="_blank">Fig. 2A to C</a>). …”

Cellular blebbing in U373MG cells induced by NK1R activation is mediated by the Rho/ROCK/MLCK pathway. by John Meshki (36999)

“…<p>(<b>A</b>) Representative cell impedance recordings of U373MG cells preincubated with control, 2.0 µg/ml C3 transferase, 10 µM Y27632 or 50 µM ML9 for 60 min and then stimulated with SP (100 nM) where indicated by <i>solid arrow</i>. …”

Lingual echolocators modify the intensity of their emissions according to the environmental complexity and the stage of target approach. by Yossi Yovel (43448)

“…<p>(A) Examples of six trials, showing that emission intensity gradually decreases with time along the approach. …”

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). …”

Dopaminergic neuron differentiation is disrupted in <i>Dkk1</i> mutants. by Diogo Ribeiro (230483)

“…Scale bar = 100 µm. Quantification in (B) revealed a 40% decrease in the numbers of dopaminergic neurons in <i>Dkk1^+/−</i> embryos when compared to <i>Wt</i> littermate controls (mean ± s.e.m- <b><i>Wt</i></b><b>:</b> 568.3±31.7, N = 3; <b><i>Dkk1^<b>+/−</b></i></b><b>:</b> 346.5±27.2, N = 8 p = 0.0015 ** unpaired t-test). …”

Forests to Faucets 2.0 by U.S. Forest Service (17476914)

“…As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. …”

Metformin reduces lipid stores and causes intestinal fluid imbalance. by Cathy Slack (251729)

“…<p>(<b>A</b>) Quantitation of triacylglycerides (TAGs) in flies treated with 0 mM, 1 mM, 10 mM and 100 mM metformin for 7 days. …”

Effect of 10 mM DCA on protein expression in mammary cell lines after 48 hours. by Tatjana P. Harting (4093507)

“…Significantly increased BAD expression was observed in all cell lines except MTH53A. Data are shown as mean ± SD, n≥3 and are presented as relative mean in comparison to untreated control (%). …”

Odor Responses of DNA-Cy3 Sensor Spots Read with Microarray Scanner by Joel White (84607)

“…Pairwise Pearson correlation coefficients ranged from 0.91 to 1.00 (mean = 0.98, SD = 0.016).</p> <p>(B) Twenty nine different DNA-Cy3 sensors and Cy3 alone (rows) tested with the same odor test set as (A) (columns). …”

Emergence of assemblies in the presence of structured external input. by Lisandro Montangie (8816753)

“…<p><b>A</b>. Mean clustering coefficient versus the pairwise correlation coefficient of the input pattern. …”

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). …”

The defects of OB in Lgl1 mutants. by Zhenzu Li (3108309)

“…(C). (0) The laminar structure of OB. (a-d) A notable decrease in density and disorganization of granule cells at p21. …”

Relationship between serum progesterone levels and top-quality blastocyst formation rate. by V. S. Vanni (4030871)

“…<p>With an increase in progesterone levels at induction, a decrease in top-quality blastocyst is observed. The decrease in top-quality blastocyst formation rate was found to be statistically significant after dividing patients into groups based on four different cut-off values of serum P levels: < or ≥ 1.0 ng/ml (p <0.01), < or ≥ 1.5 ng/ml (p <0.01), < or ≥ 2.0 ng/ml (p <0.01) and < or ≥ 2.5 ng/ml (p <0.01).…”

Exemplar press-related channel. by David T. Bundy (7586414)

“…Mean firing rates show a complex task response with firing rates significantly (p<0.05) decreased around the lever press and 650 ms prior to the reach to the pellet and then significantly increased immediately prior to the reach to the pellet. …”

<i>Apoe</i> expression is reduced in the parietal cortex and CA1 of aged mice. by Ileana Soto (531878)

“…(B) Quantification of <i>Apoe</i> hybridized area in the cortex shows a significant decrease of <i>Apoe</i> expression in the aged mice. …”

Class switch recombination analysis. by Sergio Roa (247216)

“…The average switching of the wildtype mice in each experiment was defined as 100% and the data shown represent the mean ± SD. …”

Combination Proximal Pulmonary Artery Coiling and Distal Embolization Induces Chronic Elevations in Pulmonary Artery Pressure in Swine by Jaume Aguero (731734)

“…Pulmonary vascular remodeling occurred in both embolization models, although only the combination embolization model had a decrease in pulmonary capacitance. Taken together, these cardiopulmonary hemodynamic and structural findings identify the novel combination embolization swine model as a valuable tool for future studies of chronic PH.…”

H₂S effects on kidney perfusion and oxygen consumption. by Hanno Maassen (7235339)

“…</b> Mitochondrial membrane potential in H₂S treated pig kidney mitochondria (data expressed as ratio red / green relative to control) show a 39% decrease in mitochondrial membrane potential in 100 ppm NaHS treated mitochondria compared to non-treated mitochondria. …”