Data_Sheet_1_SNP-Based Genotyping Provides Insight Into the West Asian Origin of Russian Local Goats.PDF by Tatiana E. Deniskova (5527175)

“…In addition, we addressed genetic relationships between local and global goat populations obtained from the AdaptMap project. Russian goats showed a high level of genetic diversity. Although a decrease in historical effective population sizes was revealed, the recent effective population sizes estimated for three generations ago were larger than 100 in all studied populations. …”

Data_Sheet_2_SNP-Based Genotyping Provides Insight Into the West Asian Origin of Russian Local Goats.PDF by Tatiana E. Deniskova (5527175)

“…In addition, we addressed genetic relationships between local and global goat populations obtained from the AdaptMap project. Russian goats showed a high level of genetic diversity. Although a decrease in historical effective population sizes was revealed, the recent effective population sizes estimated for three generations ago were larger than 100 in all studied populations. …”

Magnitude of within-object color coding within each CNN layer. by JohnMark Taylor (8329326)

“…Regression analyses were conducted to test for an aggregate increase or decrease in color representation over the course of processing; upward- and downward-facing arrows denote a significant increase or decrease respectively, with the level of significance denoted by the adjacent asterisks. …”

Antihypertensive effect and the underlying mechanisms of action of phytolaccagenin in rat models by Imran Ul Haq (12676309)

“…</p> <p>Intravenous administration of phytolaccagenin decreased mean arterial pressure (MAP), significantly, in normotensive and hypertensive anesthetized rats. …”

Data Sheet 1_Pregnancy and contraceptive use among participants of childbearing potential in the HVTN 705 HIV vaccine trial in Southern Africa.docx by Pamela Mda (20390050)

“…Each additional year of age was associated with an 8% decrease in pregnancy incidence (p = 0.014). Women aged 31–35 years had the lowest pregnancy incidence [1.75 (0.48, 4.48) per 100 person-years]. …”

Supplementary Material for: Urine Osmolality and Renal Outcome in Patients with Chronic Kidney Disease: Results from the KNOW-CKD by Lee M.J. (4145941)

“…This association was particularly evident in patients with CKD stages 3–4 (per 10 mosm/kg decrease; HR, 1.02; 95% CI, 1.00–1.03). …”

Long-Term Neurophysiological Outcomes in Patients Undergoing Coronary Artery Bypass Grafting by Irina V. Tarasova (14656446)

“…Postoperative decline in cognitive functions was determined by a 20% decrease in the cognitive indicator compared to that at baseline on 20% of the tests included in the Status PF battery. …”

Table_1_Benzene Exposure Leads to Lipodystrophy and Alters Endocrine Activity In Vivo and In Vitro.docx by Ying Cui (93661)

“…Male adult C57BL/6J mice were exposed to benzene at 0, 1, 10 and 100 mg/kg body weight by intragastric gavage for 4 weeks. …”

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. …”

Data_Sheet_1_Lack of Blue Light Regulation of Antioxidants and Chilling Tolerance in Basil.docx by Dorthe H. Larsen (9729479)

“…The effect may be more pronounced at a lower PPFD. We investigated the changes in primary and secondary metabolites of basil in response to the percentage of blue light (9, 33, 65, and 100%) applied either as a 5-day End-Of-Production (EOP) treatment or continuous throughout the growth cycle in the green cv. …”

Image_1_Different Renal Function Patterns in Patients With Acute Heart Failure: Relationship With Outcome and Congestion.jpeg by Alberto Palazzuoli (12203753)

“…</p>Results<p>We recruited 467 subjects with a mean congestion score of 3.5±1.08 and a median creatinine value of 1.28 (1.00–1.70) mg/dl, eGFR 50 (37–65) ml/min/m² and NTpro B-type natriuretic peptide (BNP) 7,000 (4,200–11,700) pg/ml. …”

Image_2_Different Renal Function Patterns in Patients With Acute Heart Failure: Relationship With Outcome and Congestion.jpeg by Alberto Palazzuoli (12203753)

“…</p>Results<p>We recruited 467 subjects with a mean congestion score of 3.5±1.08 and a median creatinine value of 1.28 (1.00–1.70) mg/dl, eGFR 50 (37–65) ml/min/m² and NTpro B-type natriuretic peptide (BNP) 7,000 (4,200–11,700) pg/ml. …”

Data from: Viviparity does not affect the numbers and sizes of reptile offspring by Shai Meiri (394933)

“…We attribute this paradoxical result to (1) strong selection on optimal offspring sizes, unaffected by parity mode, (2) the lack of a larval stage in amniotes, favouring large eggs even in the ancestral oviparous mode, and (3) the ability of viviparous females to decrease the mass of uterine embryos by reducing extra-embryonic water stores. …”

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. …”

Supplementary file 1_Global burden and trends of adverse effects of medical treatment, 1990–2021: an analysis from the global burden of disease study 2021.docx by Keqing Zhang (326894)

“…By 2036, it is forecast that ASIR will decrease to 72.33 (19.40–125.27) per 100,000 persons-year, and ASDR will decrease to 40.98 (21.52–60.43) per 100,000 persons-year.…”

Data_Sheet_1_Impacts of pH on the Fitness and Immune System of Pacific White Shrimp.docx by Veran Weerathunga (11522143)

“…All these results suggest that a 100-days exposure to pH 7.6 did not have a direct effect on fitness but lead to a modulation of the immune response.…”

Data_Sheet_1_Impacts of pH on the Fitness and Immune System of Pacific White Shrimp.docx by Veran Weerathunga (11522143)

“…All these results suggest that a 100-days exposure to pH 7.6 did not have a direct effect on fitness but lead to a modulation of the immune response.…”

Disease progression phenotypes, clinical cohorts, and our computational framework together with the reproducibility of disease progression pattern. by Kimiyo N. Yamamoto (213724)

“…See <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0215409#pone.0215409.s004" target="_blank">S1(E) Doc</a></b> for details.…”

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. …”

Intracellular GSH and GSH/GSSG ratio in monolayer cultures upon induced oxidative stress. by Mireille M. J. P. E. Sthijns (12245491)

“…<p>A) Exposing the monolayer cultures of 0:100, 50:50 and 100:0 INS1E:alphaTC1 cells to 500 μM H₂O₂ only induced a significant decrease in intracellular GSH compared to a monolayer culture not exposed to H₂O₂ in the ratios of 50:50 and 0:100 INS1E:alphaTC1. …”