Identification of SOCE during the earlier events of DC maturation. by Romain Félix (586765)

“…<p>DC were treated with thapsigargin (TG) in calcium free PSS, then PSS supplemented with 2 mM Ca²⁺ was added (<b>A</b>). 100 µM 2-APB PSS solution induced a rapid and reversible decrease of [Ca²⁺]_i (Grey trace). …”

RNAi-mediated knockdown of <i>AjAPN1</i> transcript and its encoded protein. by Thuirei Jacob Ningshen (485260)

“…Values represented are mean±standard deviation of three independent experiments (n = 3). …”

Ca²⁺ induced swelling in isolated mitochondria. by Jun Li (6494)

“…<p>(<b>A</b>) Representative traces of 100–1000 µmol/L Ca²⁺ induced a decrease in light transmission in the ischemia/reperfusion (I/R) 24 h group. …”

Capillary density. by Yeong-Hoon Choi (286493)

“…<p>A) Representative micrographs of left ventricular cross-sections (6 µm thickness) showing capillary vessels in all groups (as visualized by FITC-labeled lectin, scale bar represents 100 µm). …”

Oligodendrocyte ablation modifies the expression of axonal guidance molecules and neuronal plasticity markers. by Sandrine Doretto (221560)

“…Scale bar 100 µm. Quantification (expressed as % of control) of Netrin-1 shows a decrease of its expression in granule cell precursors of the external granular layer; while a concomitant increase of Sema3A expressed by Purkinje neurons is observed in MBP-TK mice versus WT siblings. …”

Time traces of the end-to-end length of DNA. by Rifka Vlijm (133750)

“…The end-to-end length of the DNA molecule remains constant after the proteins are flushed in. <b>B </b><i>NAP1, H2A/H2B:</i> At t = 0 s the force is lowered to 0.3 pN, immediately after 3.9 nM NAP1 preincubated with 2.6 nM H2A and 2.6 nM H2B is flushed in. …”

Resveratrol exhibits dual effects on adult hippocampal precursor cells <i>in vitro</i>. by Mario Torres-Pérez (840055)

“…(<b>C)</b> Cell proliferation determined by BrdU incorporation showed a significant decrease with 10 μM RVTL (*<i>p</i> < 0.001). …”

Arrhenius and Eyring analysis of McLOV proteins. by Kaley K. El-Arab (733285)

“…In contrast 337-TF, has markedly increased enthalpies of activation (71 kJ/mole vs. ~50 KJ/mole) with a decrease in the entropic penalty (-44 J/mole*K vs. ~ -100 J/mole*K).…”

Collagen distribution in transverse sections of femoral and tibial ACL insertions. by Jeffrey P. Spalazzi (453272)

“…C) Average collagen distribution within the insertion fibrocartilage, normalized for percent distance from ligament (0%) to bone (100%), revealing a gradient of collagen content across the fibrocartilage interface (<i>Blue</i> and <i>red</i> lines represent mean values and standard deviation, respectively; n=3; NFC = Non-Mineralized Fibrocartilage, MFC = Mineralized Fibrocartilage).…”

Effect of α-santalol on cell viability and proliferation. by Sreevidya Santha (381262)

“…<p>Human breast cancer cells MCF-7 and MDA-MB-231 and normal human breast epithelial cells MCF-10A were treated with either DMSO (control) or 10–100 µM α-santalol for 12, 24 and 48 h. …”

Collagen distribution in sagittal sections of femoral and tibial ACL insertions. by Jeffrey P. Spalazzi (453272)

“…C) Average collagen distribution within the insertion fibrocartilage, normalized for percent distance from ligament (0%) to bone (100%), revealing a gradient of collagen content across the fibrocartilage interface (<i>Blue</i> and <i>red</i> lines represent mean values and standard deviation, respectively; n=3; NFC = Non-Mineralized Fibrocartilage, MFC = Mineralized Fibrocartilage).…”

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 role of cell density on drug response in multilayered cell culture. by Maria Håkanson (154559)

“…Here, (i) shows cells in 90 µm wide 3D clusters at high density while (ii) and (iii) are cells cultured on the 200 µm wide (A = 3×10⁴ µm) 2D patterns at high and low density, respectively. …”

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. …”

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). …”

In the dark, <i>nob</i> mice GCs oscillate asynchronously, but light stimulation synchronizes their oscillations. by Beerend H. J. Winkelman (7377299)

“…(Bi) Each trace shows the mean normalized GC activity of 100 episodes of activity in the dark during the first 2 s of a 5-s window. …”

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. …”