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.…”

Characteristics at enrolment, Nairobi. by Faith Magut (15447266)

“…By 2019, 24% received the complete package and 21% received the partial package. We found evidence of an increase in HIV status knowledge(10.0% [95%CI:4.5,15.2]), social support(27.2% [95%CI:19.2,35.5]) and a decrease in condomless sex(-9.1% [95%CI:-13.6,-4.1]), and the proportion with ≥2 lifetime partners(-7.6% [95%CI:-12.4,-2.2]) for the complete package. …”

Layering of DREAMS interventions in Kenya. by Faith Magut (15447266)

“…By 2019, 24% received the complete package and 21% received the partial package. We found evidence of an increase in HIV status knowledge(10.0% [95%CI:4.5,15.2]), social support(27.2% [95%CI:19.2,35.5]) and a decrease in condomless sex(-9.1% [95%CI:-13.6,-4.1]), and the proportion with ≥2 lifetime partners(-7.6% [95%CI:-12.4,-2.2]) for the complete package. …”

Social support, 2019 – Sensitivity analyses. by Faith Magut (15447266)

“…By 2019, 24% received the complete package and 21% received the partial package. We found evidence of an increase in HIV status knowledge(10.0% [95%CI:4.5,15.2]), social support(27.2% [95%CI:19.2,35.5]) and a decrease in condomless sex(-9.1% [95%CI:-13.6,-4.1]), and the proportion with ≥2 lifetime partners(-7.6% [95%CI:-12.4,-2.2]) for the complete package. …”

Characteristics at enrolment, Gem. by Faith Magut (15447266)

“…By 2019, 24% received the complete package and 21% received the partial package. We found evidence of an increase in HIV status knowledge(10.0% [95%CI:4.5,15.2]), social support(27.2% [95%CI:19.2,35.5]) and a decrease in condomless sex(-9.1% [95%CI:-13.6,-4.1]), and the proportion with ≥2 lifetime partners(-7.6% [95%CI:-12.4,-2.2]) for the complete package. …”

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. …”

<i>Waddlia chondrophila</i> induce severe pneumonia. by Ludovic Pilloux (546728)

“…<i>chondrophila</i>. Magnification 100x. <b>C)</b> Ten days post-inoculation, we observed a moderate inflammation since the lympo-plasmacytic infiltration has slightly decrease, whereas there was still recruitment of macrophages in the rare areas where <i>W</i>. …”

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. …”

Table_1_Endometrial proteomic profile of patients with repeated implantation failure.docx by Jing Yang (13969)

“…Introduction<p>Successful embryo implantation, is the initiating step of pregnancy, relies on not only the high quality of the embryo but also the synergistic development of a healthy endometrium. …”

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). …”

The effector functions of CHK-152 contribute to protection <i>in vivo</i>. by Pankaj Pal (404355)

“…No statistically significant differences between ch-CHK-152 and ch-CHK-152 N297Q were observed with the 25 µg dose. Of note, we observed a reproducible decrease in clinical score on day 5 in many animals. …”

The Influence of Doctor-Patient and Midwife-Patient Relationship in Quality Care Perception of Italian Pregnant Women: An Exploratory Study by Laura Andrissi (729056)

“…<div><p>Background</p><p>The study focuses on the perceived nature / technique opposition in pregnancy and delivery emerging from gynaecologist/ midwife/ pregnant woman relationships. We developed a cross-sectional survey to identify, by means of a multidimensional data-driven approach, the main latent concepts structuring the between items correlation correspondent to the different general opinions present in the data set. …”

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. …”